DEV Community: Actian for Developers

How to Measure RAG System Performance

Oluseye Jeremiah — Sat, 28 Mar 2026 10:17:27 +0000

Your RAG demo passed every test. The dashboard showed green across the board, with answers that clearly cite source documents. A key metric called "Faithfulness" scored 0.89. Then you shipped to production. Within two weeks, 35% of users reported wrong answers. The metrics hadn't changed. The failures were real.

What happened? Test queries looked formal, "What is the enterprise pricing structure?" while production queries were casual, "How much does this thing cost?" Faithfulness, which checks whether answers rely on retrieved documents, caught the hallucinations but missed tone problems, missing context, and the dozens of ways RAG systems fail when real users show up.

Most teams add more metrics, build bigger dashboards, and measure everything, but in the end, they predict nothing. Weights & Biases found that a simple zero-shot evaluation prompt outperformed complex reasoning frameworks at 100% accuracy versus 82-90%, adding sophistication made results worse, not better. The problem isn't quantity, it's choosing the right measurements.

Engineers know evaluation is hard, and most aren't doing it well. Neptune.ai research found that many RAG product initiatives stall after the proof-of-concept stage because teams underestimate the complexity of evaluation. This article walks through selecting three to five metrics that actually predict failures: which metrics catch which problems, what each costs, and how to build monitoring that scales.

TL;DR

Most teams measure retrieval and generation but miss end-to-end user success. Systems score 0.89 on Faithfulness while 35% of users report failures because metrics don't catch tone or context mismatches. Neptune.ai found that many RAG initiatives stall after the proof-of-concept stage because teams underestimate the evaluation complexity.
Simple beats complex: Weights & Biases found zero-shot prompts hit 100% accuracy versus 82-90% for complex frameworks. Adding sophistication made results worse, not better.
Ground truth costs $50-200 per Q&A pair. Building 1,000 pairs requires $50,000-200,000. Reference-free metrics cost $0.01-0.04 per check and scale to production.
Production queries break test sets. Derive 50% from production logs, refresh quarterly, weight edge cases (5% of traffic, 40% of complaints).
Start with three metrics: Context Relevance + Faithfulness + Answer Relevance at $0.02-0.04 per query. Expand only when you hit concrete limits.

Why Generic RAG Evaluation Metrics Fail

Most RAG dashboards look convincing. Precision stays high, Faithfulness remains above 0.85, and Answer Relevance seems stable. But while the metrics show no problems, production tells a different story.

Users report incomplete answers, responses miss intent, and queries fail even though no hallucination occurs. Engineers re-run the evaluation and see the same strong numbers. The issue isn't a missing metric, it's a missing layer.

The three-layer problem

Every RAG system operates across three layers, but most evaluation pipelines cover only two.

Layer 1 (Retrieval) measures whether the system retrieved the right documents using Precision, Recall, and Mean Reciprocal Rank. These metrics assess ranking quality and coverage — if Recall drops, the system fails to surface necessary context, and if Precision drops, irrelevant documents pollute results. Retrieval metrics matter, but they don't explain why users still complain.

Layer 2 (Generation) measures whether the model used retrieved documents correctly. Faithfulness checks whether claims appear in the retrieved context, while Answer Relevance checks whether the response addresses the query. These metrics reduce hallucinations and detect context misuse, but they still miss many production failures.

Layer 3 (End-to-end user success) measures whether the answer actually helped the user. This layer covers tone, clarity, and whether the system actually completes the user's task. Automated metrics rarely capture this layer.

A system might report a Faithfulness score of 0.89 and context relevance of 0.91, yet 30-35% of production queries still fail. The model grounds its answers, retrieval works as expected, and there are no clear hallucinations. The failure stems from a query mismatch.

Most teams measure the retrieval and generation layers, but not the full end-to-end alignment. Understanding the three layers narrows the problem. The next question is which you can actually monitor in production without ground truth?

Reference-Based vs. Reference-Free

Once you recognize the three-layer structure, the question emerges, "Do you have ground truth Answers?" This limitation affects which metrics you can use, how much evaluation will cost, and whether you can monitor continuously.

Reference-based metrics compare system output against known correct answers. Context Recall, Context Precision, and Answer Correctness require labeled datasets. Their strength is stability for regression testing; they let you benchmark precisely and spot problems as models change.

However, creating high-quality ground truth typically costs $50-200 per Q&A pair for expert annotation and quality assurance, particularly for specialized domains. At this rate, a 1,000-query test set costs $50,000–200,000, so reference-based evaluation doesn't scale to continuous production monitoring.

Reference-free metrics don't require labeled answers. Faithfulness, Answer Relevance, and Context Relevance estimate correctness by comparing outputs to retrieved context. Their main advantage is that they scale easily, making them practical for ongoing production monitoring.

Most production systems need both types. Use reference-based metrics to set baselines, and reference-free metrics to monitor daily performance.

With this foundation in place, let's look at the specific metrics you'll use, what they measure, when they might fail, and which problems they help catch.

Core Metrics Explained

Most teams use whatever metrics their framework provides. The issue isn't that these metrics are wrong, but that they're often used without a clear understanding of what they measure or where they might fail. Retrieval determines which information the model receives. If retrieval fails, the generation step can't fix it.

Context Precision

Measures how many retrieved documents are relevant. If your retriever returns five documents and only two contain useful information, precision drops to 0.4.

Real failure example: an "enterprise pricing" query returns a blog post first, while the actual pricing page is ranked fifth, so the user sees incorrect information upfront. This is why Precision should be used when evaluating ranking quality, as it directly impacts the accuracy of the answers.

Context Recall

Requires you to know in advance which documents the system should retrieve for each query. This means maintaining a labeled test set where you've manually tagged, "For this question, these three documents are the correct answers."

This makes Recall valuable for regression testing: "Did our update break Retrieval?" It doesn't work for production monitoring; you can't manually label thousands of daily queries.

Context Relevance

Relies on embedding similarity to measure how close retrieved documents are to the query in the vector space. This works well for drift detection if average similarity drops over time, embeddings or indexing may be degrading. However, similarity doesn't guarantee usefulness. Treat context relevance as a monitoring signal, not a correctness guarantee.

Mean Reciprocal Rank (MRR)

Measures how high the first relevant document appears. If the first relevant result appears at position one, MRR equals 1.0. At position three, MRR equals 0.33.

> Formula: MRR = 1 / rank_of_first_relevant_result

Research suggests relevance in the top three positions predicts answer performance better than top-ten coverage.

Faithfulness

Evaluates whether the claims in a response are supported by the retrieved context. Most approaches break the answer into individual statements and verify them against the source documents. These checks typically cost between $0.01 and $0.04 apiece.

Real failure example: the system claims "coverage includes international shipping," even though the documentation only mentions domestic. Faithfulness is one of the most reliable ways to detect hallucinations, but it doesn't measure usefulness. A response can be fully grounded in the source material and still fail to help the user.

Answer Relevance

Measures whether a response actually addresses the user's question. Many implementations approach this indirectly by asking an LLM to infer the likely question from the answer, then comparing it to the original query.

The RAGAS (Retrieval-Augmented Generation Assessment Suite) paper notes that Answer Relevance often diverges from human scoring in conversational cases.

Real failure example: a user asks how to reset a password, but the system responds with an explanation of the account creation process.

Answer Correctness

Compares the model's output to a gold reference answer. It provides strong regression guarantees, but requires curated ground truth, typically costing $50 to $200 per Q&A pair. Use it when precision matters more than scale.

BLEU and ROUGE

BLEU (Bilingual Evaluation Understudy) and ROUGE (Recall-Oriented Understudy for Gisting Evaluation) were designed for machine translation and measure word overlap between generated text and reference answers. They work well for translation, but break down for RAG. Two answers can convey the same meaning with different wording and still score poorly, while a hallucinated answer that mirrors the reference phrasing may score highly. Treat these metrics as rough development signals only, not as a substitute for real evaluation in production.

Metric comparison

Cost estimates reflect approximate LLM API charges for automated evaluation calls. Metrics listed as "Free" use deterministic computation with no API dependency.

Metric	Requires ground truth?	Cost per eval	Production-ready?	Best use case
Context Precision	Document labels	$0.001-0.01	Yes	High-volume monitoring
Context Recall	Document labels	$0.01-0.02	No	Regression testing
Context Relevance	No	$0.001-0.01	Yes	Continuous monitoring
MRR	Document labels	Free	Yes	FAQ systems, search ranking
Faithfulness	No	$0.01-0.04	Yes	Hallucination detection
Answer Relevance	No	$0.01-0.02	Yes	Query-answer matching
Answer Correctness	Reference answers	$50-200	No	Benchmark testing
BLEU/ROUGE	Reference answers	Free	No	Development proxy only

Table 1: Comparison of RAG evaluation metrics by cost, ground truth requirements, and production readiness.

It's important to note that these metrics don't require gold-standard reference answers. However, they do rely on relevance labels for retrieved documents, which must be manually annotated. Only Context Relevance, Faithfulness, and Answer Relevance are truly reference-free.

LLM-as-a-Judge

At some point, most teams reach the same conclusion: "If automated metrics miss tone and alignment, why not let another LLM evaluate the output?"

This approach, known as LLM-as-a-judge, has become popular for evaluating RAG systems. It offers flexibility, requires no ground truth, and can capture nuanced reasoning. In practice, this method comes with trade-offs.

LLM-as-a-judge uses a large model like GPT-4 or Claude to evaluate another model's output. You provide criteria directly in the prompt: "Does the context support the answer"? "Does it address the user's question"? "Is the tone appropriate"?

The model returns a score or classification. This works well for nuanced checks and avoids the cost of creating labeled datasets. How reliable it is depends completely on how you design the prompts and how the model behaves.

The surprising finding

Weights & Biases evaluated multiple LLM-based approaches. A simple zero-shot prompt achieved 100% accuracy. More complex frameworks using reasoning chains scored 82-90%.

The simpler prompt outperformed the "smarter" ones. Complex reasoning chains introduced over-analysis. The judge inferred errors that didn't exist. It penalized acceptable variations and produced inconsistent results.

Making evaluations more complex doesn't always improve them. Sometimes, it actually makes them worse.

Known limitations include version dependency (GPT-4 and GPT-4o may produce different judgments), prompt sensitivity (small wording changes can shift scores by 10-15 points), and context length constraints (LLM-based evaluations struggles with long contexts).

Cost reality

Assume GPT-4o costs $0.015 per evaluation

1,000-case evaluation: $15 per metric
Five metrics: $75
Ten tuning rounds: $750
Monthly regression testing: $250/month, or $3,000 annually

For high-traffic systems, continuous evaluation can be expensive. LLM-as-a-judge doesn't remove the cost; it just moves it from labeling to inference.

LLM-as-a-judge works best for development iteration, qualitative validation, sample-based production review (10-20% traffic), and early-stage systems without ground truth. Avoid relying on it for compliance documentation, high-volume per-query evaluation, or benchmark comparisons across model versions.

Once you understand these basics, the real question becomes: Which metrics should you actually use? The answer depends on your specific use case and constraints.

Building Your Strategy

Which three to five metrics will predict failures in your system? There's no one-size-fits-all answer. Begin by identifying the type of failure you absolutely can't accept.

For Q&A chatbots facing hallucinations and intent mismatch risks, use Faithfulness (catches hallucinations), Answer Relevance (ensures query addressed), and Context Precision (reduces noise). Skip Context Recall since coverage is less important than accuracy. Add latency P95 and token cost.

For document search where ranking quality matters most, use MRR (position of first relevant result), Context Precision (clean ranking), and Context Relevance (embedding quality). Skip generation metrics since this is about search, not generating answers. Add result diversity. Qdrant research shows that top-three ranking quality correlates more strongly with outcome than broader retrieval depth.

For long-form generation facing drift in framing or emphasis, use Faithfulness (grounding check), Answer Correctness (if ground truth exists), and Context Coverage (percentage of retrieved context used in answer). Add coherence checks and regular human reviews since automated metrics can't guarantee the narrative makes sense.

For compliance/legal systems where omission is the dominant risk, use ALL retrieval metrics (complete coverage required), Faithfulness (no deviation), and Answer Correctness (requires ground truth). Add human validation and an audit trail. Reference-based evaluation and logging are essential for operations.

After identifying the failure mode, constraints become the second filter. Whether you have ground truth data changes everything.

The amount of traffic also matters. If your system handles hundreds of queries a day, you can evaluate each one with LLM-as-a-judge, but if you have tens of thousands, you'll need to use sampling. Budget is another factor. LLM-as-a-judge seems cheap per evaluation, but costs add up quickly when you use it for many metrics and rounds.

Most production RAG systems operate effectively with three core signals. Start with Context Relevance (cheap, continuous retrieval monitoring), Faithfulness (catches hallucinations), and Answer Relevance (ensures query addressed). Add operational metrics like Latency P95/P99 and token cost per query. Evaluation metric overhead should add no more than 10-20% to your base retrieval-plus-generation latency. Cost: $0.02-0.04 per evaluation.

Expand only after these stabilize: Have ground truth? Add Context Recall and Answer Correctness. Need compliance? Add human validation. Ranking matters? Add MRR. Avoid the temptation to measure everything — having too many metrics creates noise, which can obscure important changes.

Production Monitoring

Evaluation looks controlled in development. You curate test queries, control context, and metrics that behave predictably. Production removes those guarantees.

Real users introduce typos, vague phrasing, and inconsistent terminology while query distribution shifts and edge cases surface. In development, most queries look like your test set, but in production, most may not.

Three forces reshape performance: Query distribution shifts (users ask shorter, more casual questions and expect the system to infer intent), data evolves (knowledge bases update, new documents enter the index, embedding distributions change), and user expectations increase (people are less forgiving of slow responses or wrong tone than of small factual errors).

Continuous strategy

Evaluating in production needs a layered approach to monitoring.

Always On (Per-Query)

Context Relevance (low-cost drift detection)
Latency P95/P99 (infrastructure pressure)
Token cost per query (prompt creep)

Batch/Sampling

Faithfulness (nightly batch on query subset)
LLM-as-a-judge (10-20% traffic sample)
Human review (50-100 queries weekly)

Your evaluation process must adapt as traffic grows. If your system handles 500 queries a day, you can check them all. If it handles 50,000, that's not possible.

Setting alert thresholds

Set your thresholds before any incidents happen:

Context Relevance < 0.7: Retrieval drift likely
Faithfulness < 0.8: Hallucination risk increased
P95 latency > 2 seconds: Infrastructure constraints
User feedback < 4.0/5.0: Tone or completeness issues

def monitor_rag_health(query_results):
    """Production monitoring with threshold alerts"""
    # calculate_metrics expects: {'query': str, 'contexts': List[str], 'answer': str}
    # Returns: {'context_relevance': float, 'faithfulness': float, 'latency_p95': float, 'user_feedback': float}
    metrics = calculate_metrics(query_results)

    alerts = []

    if metrics['context_relevance'] < 0.7:
        alerts.append("Retrieval degrading")

    if metrics['faithfulness'] < 0.8:
        alerts.append("Hallucination risk")

    if metrics['latency_p95'] > 2.0:
        alerts.append("Infrastructure issue")

    if metrics['user_feedback'] < 4.0:
        alerts.append("UX problem")

    return alerts

Evaluation costs should grow more slowly than your traffic does. Sample 5-10% of queries for expensive metrics, cache embeddings, batch LLM evaluations overnight, and use smaller models for screening.

Framework Selection

Most teams shouldn't build an evaluation from scratch. Frameworks exist because evaluation becomes brittle quickly. Choose based on lifecycle stage, not feature count.

RAGAS

RAGAS (Retrieval-Augmented Generation Assessment Suite) introduced a structured, reference-free RAG evaluation. It formalized Faithfulness, Answer Relevance, and Context Relevance in a reusable format.

Strengths

Research-backed methodology
Native support for reference-free metrics
Clean integration with LangChain

Limitations
Limited explainability for metric failures
Sensitive to LLM version differences

Setup: 1-2 hours | Cost: Free + LLM API | Best for: Early-stage RAG validating retrieval and grounding quality

from ragas import evaluate
from ragas.metrics import faithfulness, answer_relevance
from datasets import Dataset

# Prepare evaluation data
data = {
    "question": ["What is the capital of France?"],
    "answer": ["Paris is the capital of France"],
    "contexts": [["France is a country in Western Europe with Paris as its capital"]]
}
dataset = Dataset.from_dict(data)

# Run evaluation
results = evaluate(
    dataset=dataset,
    metrics=[faithfulness, answer_relevance]
)

print(results)
# Output: {'faithfulness': 0.95, 'answer_relevance': 0.88}

RAGAS is a good choice if your main goal is structural correctness, rather than production monitoring. You can find full documentation on GitHub.

DeepEval

DeepEval approaches evaluation like test engineering. It supports CI/CD integration and automated regression testing.

Strengths

Broad metric library (50+ metrics)
Better failure inspection
Designed for automated pipelines

Limitations

Higher configuration overhead
More complex onboarding

Setup takes about 2-3 hours. It's open source, with optional paid tiers. It's best for teams that want to include evaluation in their release workflows.

TruLens

TruLens focuses on simplicity. It tracks groundedness, Context Relevance, and Answer Relevance without heavy configuration.

Strengths

Quick to deploy (under 1 hour setup)
Minimal configuration
Clear mental model

Limitations

Smaller ecosystem
Less extensible for advanced workflows
Slowed development pace following the Snowflake acquisition with ecosystem growth stalled

Arize Phoenix

Phoenix emphasizes production observability over development-only evaluation.

Strengths

OpenTelemetry integration
Trace-based debugging
Real-time monitoring

Limitations

Requires infrastructure integration
Heavier operational footprint
Best for mature systems that need large-scale drift detection

LangSmith

LangSmith integrates tightly with LangChain environments. It combines tracing with evaluation.

Strengths

Native LangChain support
Experiment tracking
Production trace inspection

Limitations

Ecosystem dependency
Less framework-agnostic

Best for teams using LangChain who are moving toward structured monitoring.

Framework comparison

Framework	Best for	Strengths	Limitations	Cost	Setup Time
RAGAS	Pure RAG evaluation	Reference-free, LangChain integration	Limited explainability	Free + LLM API	1-2 hours
DeepEval	Engineering teams	50+ metrics, CI/CD integration	Learning curve	Free + optional $49-299/mo	2-3 hours
TruLens	Getting started	3 core metrics, simple	Limited traction	Free	30 min
Arize Phoenix	Production debugging	OpenTelemetry compatible	Enterprise complexity	Usage-based	3-4 hours
LangSmith	LangChain users	Native integration	Vendor lock-in	Usage-based	1-2 hours

Table 2: Comparison of RAG evaluation frameworks by use case, features, and operational requirements.

Choose by phase

POC: RAGAS or TruLens
CI/CD integration: DeepEval
Production monitoring: Phoenix or similar observability tools
Enterprise governance: Commercial platforms with audit features

A good framework integrates smoothly, gives stable results across LLM versions, keeps costs predictable, and makes failures easy to spot.

Even with the right framework, teams often make the same mistakes. Spotting these patterns early can save you months of extra work.

Common Pitfalls

Most RAG evaluation failures follow predictable patterns.

Over-indexing on automated metrics

This happens when automated scores look healthy but users complain. A system reports Faithfulness at 0.92, but user feedback indicates responses feel robotic or miss conversational nuance. Automated metrics measure grounding but don't measure tone.

Fix: Allocate 10-20% of the evaluation budget to human review. Sample high-risk queries weekly. Use findings to adjust prompts or refine automated thresholds.

Test-production mismatch

This occurs when tests pass, but production fails at 40%. Test datasets contain formal queries: "What is the enterprise pricing structure?" Production users ask: "How much does this cost?" The distribution mismatch creates a silent evaluation failure.

Fix: Derive 50% of your test set from production logs. Refresh quarterly. Query patterns evolve faster than curated datasets.

Ignoring edge cases

Common queries work but rare queries fail 80% of the time. Edge cases represent 5% of traffic but generate 40% of complaints. Test sets skew toward frequent queries.

Fix: Ensure equal representation of query types in evaluation. Weight infrequent but high-impact scenarios appropriately.

Actian VectorAI DB Advantages

Most RAG evaluation pipelines expose queries and documents to external APIs. Embeddings travel to OpenAI, faithfulness checks route through Claude, and each evaluation step introduces data movement. For teams with compliance requirements, this setup doesn't work.

Actian VectorAI DB addresses this gap by allowing you to run all evaluation workloads on-premises. Queries remain local, documents never leave controlled infrastructure, and LLM-based evaluation executes using locally hosted models. This eliminates external API dependencies entirely.

Teams working with HIPAA-regulated data, financial records, or proprietary research can evaluate RAG systems on real production data without creating audit risk. Cloud evaluation costs scale with query volume and token count. Actian uses flat licensing with no per-query charges, making costs predictable as evaluation scales.

Development environments often use mocked dependencies and synthetic data. Actian allows testing with the same database engine production uses, ensuring retrieval latency, index behavior, and evaluation results accurately predict production performance.

Final Thoughts

More metrics don't guarantee better results. Automated scoring and human review form a more reliable system than either alone. Production queries provide better test coverage than curated datasets. Monitor continuously, not episodically.

The Weights & Biases benchmark confirmed that simple evaluation, done consistently, outperforms complex evaluation done occasionally. Build your strategy on that principle. The goal isn't choosing the trendiest framework or the most complex dashboard, it's building infrastructure that remains accurate, scalable, and cost-effective as query volume grows.

For teams building production RAG systems, start with three core metrics. Expand when you hit concrete limits, not hypothetical ones.

If you need on-premises evaluation without exposing sensitive data to external APIs, Actian VectorAI DB lets you run all evaluation workloads locally within your own infrastructure.

Why Real-Time Analytics Can’t Depend on Cloud in 2026

Hitesh Jethva — Fri, 27 Feb 2026 11:44:40 +0000

If your system needs to react in milliseconds, a half-second delay is no longer "almost real-time"; it is a failure. For example, in robotic welding systems, the controller has to adjust torque in under 10 milliseconds to avoid structural defects. For self-driving warehouse forklifts, obstacle detection must trigger braking within 20 milliseconds to prevent crashes. In ICU monitoring, arrhythmia detection should send alerts immediately, not 400 milliseconds later.

This is the reality many teams are discovering in 2026. Systems that look fine on paper stop behaving as expected when organizations try to run real-time analytics on cloud-based platforms.

For years, industries have been told that cloud solutions are perfect for data management, transfer, and analysis. The thought process was simple: send data to the cloud and it will process and respond faster. But in practice, these assumptions are starting to fail. AI workloads are forcing companies and experts to rethink cloud-era assumptions.

As real-time analytics shifts from basic reporting and dashboards to instant decision-making, speed becomes critical. Cloud analytics is good for reporting, but distance and network delays make it slow for time-critical actions.

Edge computing changes this model. It processes data close to where it is generated, allowing systems to make immediate decisions at the source instead of waiting for a response from a remote cloud data center. By 2030, latency-critical applications will increasingly shift to edge processing, while cloud remains dominant for batch analytics and reporting.

In this post, we will cover why cloud-based real-time analytics fails and how engineers can make architecture decisions based on actual latency limits and physical constraints.

The Real-Time Analytics Promise Versus the Physics Problem

By early 2026, almost every analytics platform claims to support real-time analytics. Cloud data warehouses talk about real-time ingestion, and major streaming data platforms promise sub-second processing. If we analyze the current situation from a marketing viewpoint, it seems that the problem has already been solved.

In reality, it hasn’t.

Several analytics platforms and cloud vendors still define anything under a second “real time” because that is what cloud infrastructure can reliably deliver. That may be acceptable for business intelligence, but it falls short for systems that require true instant response, such as manufacturing control loops, safety-critical systems, and autonomous machines.

These systems don't need insights "soon." Instead, they demand decisions "now." In these environments, latency is not a basic performance metric but a quality constraint.

This is where physics enters.

Physical distance creates a minimum latency that software cannot remove. Light moves at about 300,000 kilometers per second in a vacuum, but in fiber-optic cables, signals travel closer to 200,000 kilometers per second because of refraction and signal processing. Even a few thousand kilometers of round-trip travel can take tens of milliseconds. When you include routing, serialization, queuing, and processing delays, total latency in real-world situations often reaches 200 to 500 milliseconds.

In a cloud workflow, that physical distance becomes part of the latency budget — meaning that before any computation begins, a significant portion of the response window has already been consumed.

The promise of real-time analytics collides with physics the moment decisions happen faster than the cloud can respond. And in 2026, as AI-powered systems increasingly move from generating insights to automatically triggering operational decisions, more teams are running headfirst into that limit.

What Does “Real-Time” Mean?

The term real-time analytics refers to analyzing data as it becomes available. But some practitioners carry a different definition or opinion for the term "real-time analytics." Generally, this term is used by marketing team experts, business users, application developers, and control-system engineers.

However, each of these groups works within a different latency expectation, and when those differences are not clearly defined, systems often get designed around timing assumptions that fail once deployed in real-world environments.

To understand why this matters, it is best to look at real-time analytics as a spectrum and not a single promise.

The Spectrum of Real-Time Requirements

Real-time is not a single fixed standard; different business and technical systems operate across distinct latency tiers, each with very different architectural needs.

Business Real-Time (Sub-Second)

In this latency tier, real-time cloud analytics platforms are strongest. Dashboards, operational reporting, alerting systems, and executive monitoring systems that refresh every hundred milliseconds fall under this category. For business intelligence and reporting, sub-second latency feels like real-time.

Interactive Real-Time (Sub-100ms)

In this latency tier, web applications, recommendation engines, and in-app feedback loops fit best. These often require responses under 100 milliseconds. Cloud architectures can meet this requirement, but congestion and jitter frequently make consistency difficult.

Control Real-Time (Sub-10–20ms)

Manufacturing automation, safety systems, robotics, and autonomous machines require responses in 10 milliseconds or less. At that speed, delays are not inconvenient, they are costly. They can cause defective products, equipment damage, safety risks, or failed control responses. Cloud-based real-time analytics often fails to meet this bar because network transit alone consumes the entire latency budget.

In these scenarios, timing is everything. A robotic arm that is 300 milliseconds late can miss a weld. A safety system that reacts too slowly, may fail to prevent an accident. The question is not whether the cloud is fast. It is whether it is fast enough for the physical process it controls.

How Traditional Batch Analytics Differs

Batch analytics processes historical data on a schedule. Data is collected, stored, and analyzed hours or days later. It is ideal for forecasting, trend analysis, and long-term planning.

Real-time analytics runs continuously. It ingests live data streams, evaluates events as they happen, and decides whether immediate action is required.

The difference becomes decisive when action must occur inside a strict time window. If a defect must be rejected within 20 milliseconds, a response at 300 milliseconds is useless. Batch analytics still drives trends and planning, but when decisions must shape live systems, timing determines the outcome.

The Real-Time Analytics Technology Stack

Modern real-time stacks rely on streaming platforms for ingestion. Kafka, Flink, and Kinesis move events continuously, feeding databases built for high-throughput writes and fast reads, often with columnar storage and in-memory processing.

On top sits event-driven architecture, where actions trigger the moment conditions are met. But this only works if latency targets are defined from the start. Without a clear definition of real time, teams build systems that look modern and sound fast, but fail at millisecond response. That means fraudulent transactions slip through, vehicles brake too late, defective products pass inspection, or safety systems react after the damage is done.

Why Does Cloud Architecture Create Unavoidable Latency?

Cloud latency is governed by distance, transmission paths, and routing physics more than system settings. For dashboards and report refreshes, the cloud feels fast. But once you examine how cloud processing actually works, it becomes clear why some real-time use cases hit a hard limit.

The Cloud Processing Pathway

Even for experienced engineers, it’s important to recognize that cloud-based processing follows a multi-step loop, not a direct path. The diagram above illustrates the full round-trip workflow:

Data generation (on-premises layer): Raw data originates at the device level — sensors, cameras, PLCs, or industrial controllers.
Local aggregation: A local gateway, industrial PC, or PLC filters, normalizes, and prepares the data before it leaves the facility.
Internet transmission: The data is encrypted and transmitted over the public or private internet to a cloud region.
Cloud ingestion & queuing: Services such as Azure IoT Hub or AWS Kinesis receive the stream and buffer it for processing.
Processing & analytics: The cloud platform runs analytics, inference, or rule engines to generate a decision or insight.
Local action: The local system executes a physical response — stopping a machine, rejecting a product, triggering an alert, or adjusting an actuator.

In this workflow, every step adds latency. Even with optimized pipelines, data moves through dozens of network hops, routers, and firewalls before it reaches its destination. Geographic distance adds another layer. A factory on the U.S. East Coast will reach a nearby cloud region such as AWS us-east-1 faster than one sending data across the country to AWS us-west-2.

But even the closest cloud data center is still physically distant. That distance alone is enough to introduce noticeable delays.

Latency Components You Can't Eliminate

Some sources of latency are unavoidable, for example:

Physical propagation delay: Data travels through fiber at a fraction of the speed of light. Crossing the U.S. coast-to-coast takes roughly 100 milliseconds round-trip before any processing happens at all.
Router processing at each hop: Each router introduces a processing delay, often microseconds, that adds up across 10–20 hops.
Serialization and deserialization: Data must be packaged, encrypted, decrypted, and unpacked.
Security: TLS handshakes and inspection add delay.
Queueing delays: Cloud ingestion services buffer incoming data, especially under load.

Even if you consider an ideal condition, cloud round-trip latency might drop below 150–200 milliseconds, but still can't match real-time for manufacturing control loops.

When Cloud Optimization Isn't Enough

Cloud providers offer optimizations, but the gap remains large. Content delivery networks and edge caching help with static content, but they fail in live data processing and real-time decision-making.

Deploying workloads in nearby regions shrinks geographic distance without removing the network round trip. Dedicated connections such as AWS Direct Connect reduce jitter and packet loss, but they cannot overcome the baseline latency physics imposes.

Some teams try multi-region architectures to get closer to users or devices, but this adds complexity without fixing the core issue. For systems that need responses in 10–20 milliseconds, having a few milliseconds off a 300 milliseconds round trip doesn’t change the outcome.

The Manufacturing Floor Reality Check

Cloud latency is abstract until you attach real numbers. Manufacturing shows clearly why cloud-based real-time analytics breaks down. Let’s do the math.

There is a production line that produces approximately 400 units per minute at 6.67 units per second, which means roughly every 150 milliseconds, a unit passes the inspection point. It is important for each system to respond within the set window, or it might be too late.

Comparing this figure with cloud-based AI or analytics that usually takes 300–500 milliseconds for a full round trip, the score comes to 3.3 units (500ms/150ms). By the time the cloud responds, 3–4 units would already pass the inspection.

Now, apply this timing gap to critical manufacturing tasks such as defect detection, weld monitoring, and PCB inspection, where decisions must happen before the next unit reaches the station.

Quality Control at Production Speed

Automated quality checks are common in modern factories. Vision inspection systems perform the scanning for defective products, weld monitoring systems are responsible for quality assessment while the weld is still happening, and Printed Circuit Board inspection lines analyze boards as they move through production.

Each of these systems operates fast, but the decision window is minimal.

With cloud-based processing, even a small delay of 500ms can allow defective parts to pass the inspection point. By the time the system flags it, halting the parts may no longer be possible.

However, edge processing changes the equation with responses in under 10 milliseconds, i.e., 50x faster. This responsiveness enables instant rejection of defective units, thereby improving quality control.

Safety Monitoring That Actually Protects Workers

Safety systems demand even stricter requirements. Delay in time is dangerous for workers.

If a worker steps into a hazardous zone without proper protective equipment and the sensors or cameras fail to trigger an immediate alert, the worker is exposed to serious injury or contamination.

After a 500 milliseconds delay, the system sends the alert to the cloud, but by then the worker has already entered the contaminated area. Cloud-based analytics can reconstruct the incident timeline, but only edge-based analytics can prevent it.

Predictive Maintenance Windows

Most systems and machines give alerts or warning signs before complete failure. For example, vibration anomalies, temperature change, and acoustic patterns. Generally, there is a 1–2 second window period between early detection and actual damage.

Cloud analytics can detect issues, but the system often responds too late. Edge analytics processes events immediately and stops the operation before real damage occurs.

Beyond Manufacturing

Manufacturing is not the only example of cloud latency. Many industries now demand immediate, analytics-driven decisions and face the same constraints. Let’s examine a few others.

Healthcare: When Seconds Determine Outcomes

Healthcare systems rely on real-time analytics to monitor patients. ICU sensors track oxygen levels, heart rate, and other vital signs. This data only delivers value when the system detects anomalies early and flags a potential emergency.

Delays in data transmission or cloud processing directly affect patient outcomes. Healthcare organizations must also meet strict regulatory requirements. HIPAA and data residency laws often require sensitive patient data to remain on-premises or within controlled environments, making edge processing a practical and compliant solution.

Autonomous Systems: Physics Won't Wait for the Cloud

Sales of self-driving vehicles continue to rise, driven by systems that detect obstacles, predict motion, and decide how to respond in milliseconds. Industrial robots, drones, and automated guided vehicles in warehouses use similar capabilities to generate real-time insights.

A 100–200 milliseconds delay in an autonomous system can mean a missed brake command or a collision. If connectivity drops, decision-making can stall entirely.

Autonomous systems must process data locally and keep response times under 50 milliseconds. The cloud supports model training and optimization, but real-time control must remain at the edge.

Financial Services: Fraud Detection at Transaction Speed

For financial institutions, timing is critical. Delays influence customer behavior and lead to significant data and revenue loss. Whether a credit card transaction, account login, payments, or any other financial move, the data must be evaluated under 100 milliseconds.

A delay in fraud detection can allow illegitimate transactions to complete or cause valid transactions to fail. In high-frequency trading, where decisions occur in microseconds, cloud latency is not viable.

Many financial institutions use a hybrid model: fast risk scoring and decision logic run at the edge or on-premises, while deeper analysis and model training run in the cloud.

The Connectivity Assumption Cloud Vendors Won't Discuss

Most cloud-based real-time analytics platforms assume constant internet connectivity. Many data flow diagrams show seamless movement from device to cloud and back. In reality, when the network fails, business operations fail with it.

When real-time analytics depend on a constant cloud connection, the gaps may eventually turn into system failure.

When Connectivity Isn't Reliable

The internet is widely available, but reliability is not guaranteed. Many locations cannot depend on stable, low-latency connections.

Consider retail stores where an outage takes point-of-sale systems offline during peak hours or a major sale, and transactions stop immediately.

Industrial IoT deployments often operate in remote locations such as mines, oil fields, and factories, where latency spikes and packet loss are common. Even well-connected urban areas experience peak-time congestion that introduces unpredictable delays.

In all such cases, cloud-based real-time analytics will slowly fail, and there will be a delay in operations and decisions.

The optimal solution is to move to edge computing in these cases to operate normally, even if the network is slow or unstable.

When Connectivity Is Prohibited

Some environments do not allow cloud connectivity due to security and compliance requirements.

Manufacturing plants often use air-gapped networks to protect their intellectual property and prevent outside access. Financial institutions must comply with data sovereignty laws that govern where sensitive data is stored and processed. Healthcare organizations must meet HIPAA rules that set limits on how and where patient data is stored and shared.

In all such cases, organizations need to process real-time analytics on-site or at the edge. Hybrid and private cloud models can handle less critical tasks, but operations that require low latency or follow strict rules must stay on-premises.

The Sync-When-Connected Pattern

Many teams use a straightforward approach where they process data at the edge and sync it to the cloud when a connection becomes available.

Edge systems make real-time decisions on-site, so operations keep running even during outages or network slowdowns. Once the connection is stable again, the system sends logs, summaries, and model updates to the cloud for deeper analysis and retraining.

This method gives you quick local responses while also letting you use the cloud’s scale and advanced analytics.

The Architecture That Actually Works (Cloud-Right, Not Cloud-First)

Many organizations learned a hard lesson and now shift from cloud-first thinking to cloud-right architecture. They once pushed real-time workloads to the cloud because it sounded simple, but that approach ignores latency, connectivity, compliance, and cost.

A more practical approach is what Deloitte describes as cloud-right, where each workload is placed in the location that best fits its needs.

Cloud Tier: For Elasticity and Deep Analytics

The cloud is still the best place for workloads that benefit from scale and flexibility. They excel at training machine learning models, for example, often requiring large GPU clusters that are expensive to run on-premises. Historical data analysis, reporting, and long-term storage are also a perfect fit, where data lakes and warehouses can scale on demand.

The cloud works well for variable workloads, experimentation, and analytics that don't require immediate responses. If sub-second latency is acceptable, cloud-based processing is usually the most cost-effective option.

On-Premises Tier: For Consistency and Compliance

On-premises systems are best for predictable, high-volume inference workloads that run continuously and would be costly to execute in the cloud. They play a key role when data is regulated and cannot leave the premises due to compliance, security, or data sovereignty requirements.

On-premises deployments offer consistent performance and tighter integration with existing enterprise systems. For use cases that need reliable sub-100 milliseconds responses but don't require ultra-low latency, on-premises strikes a perfect balance.

Edge Tier: For Immediacy and Offline Capability

Edge real-time processing is essential for control systems, safety applications, and autonomous operations that require sub-10-20 milliseconds latency and offline capability. Data driven decisions are also possible in such cases when connectivity is slow, unreliable, or completely unavailable. Also, it allows data to be analyzed where it's generated, reducing bandwidth costs, improving operational efficiency, providing actionable insights, and avoiding cloud round-trip entirely.

What to Do About It?

Real-time analytics have different meanings for different contexts. You don't need to chase cloud or edge computing for better results. Instead, you must run an assessment of your requirements and constraints before choosing an architecture.

Step 1: Define Your Real-Time Requirements

Start by writing down what real-time actually means for your use case. Be specific and use numbers. For business processes that need only dashboards and reporting raw data or data visualizations, sub-second responses are usually fine — cloud data analytics would fit best.

Interactive applications, where users expect instant feedback, often need responses under 100 milliseconds. This is where cloud performance becomes marginal and needs careful testing.

Manufacturing automation, robotics, and machine-driven decisions typically need responses under 20 milliseconds. Safety systems are even stricter, often under 10 milliseconds.

Before evaluating real-time analytics tools or platforms, define your latency SLAs clearly.

Step 2: Assess Connectivity Constraints

Ask if you need a system that must operate during outages, in remote locations, or under regulatory restrictions. Be honest with yourself.

Retail locations, mobile systems, remote industrial sites, and field operations regularly deal with outages and unstable networks. Ask yourself if you need a system that continues operating when offline. These constraints often matter more than raw performance benchmarks.

Step 3: Match Requirements to Architecture

If your real-time analytics requirements demand sub-100 milliseconds latency, offline capability, or on-premises deployment, cloud-only solutions are insufficient. You might require hybrid or edge-based architectures.

You can even switch to platforms like Actian’s VectorAI DB (beta in January 2026), designed to support edge and on-premises deployments specifically for latency-critical workloads.

The Bottom Line

Cloud vendors didn't break real-time analytics. Physics did.

For cloud vendors, real-time often means dashboards that refresh quickly or data that appears within a second. For industrial engineers, real-time means systems that react in milliseconds. That gap in real-time analytics definition matters.

No amount of optimization can change the physics involved. Data still has to travel across networks, through routers, and into distant data centers. A 500 milliseconds cloud round-trip might feel fast in software terms, but it is 50x too slow for manufacturing control systems that need responses in under 10 milliseconds.

That's why many real-world applications simply cannot depend on the cloud for real-time processing. In 2026, the most successful real-time analytics systems will not be cloud-first. They will be physics-based architecture: edge and on-premises deployments. They exist because some decisions must be made immediately.

If your real-time analytics requirements demand sub-100 milliseconds latency, offline operation, or strict data residency, cloud-only architectures start to break down. Solutions designed for edge and on-premises deployment, such as Actian’s VectorAI DB entering beta in January 2026, are built specifically for these constraints.

What's Changing in Vector Databases in 2026

Praise James — Tue, 17 Feb 2026 14:25:14 +0000

The vector database market has shifted. Engineering conversations have matured from “use Pinecone” to “we can build this on PostgreSQL." What the market is witnessing is a growing movement from cloud-native vector databases back to traditional infrastructure, where embedding vector search directly into a relational database has become standard practice.

Every major cloud provider and traditional database, from AWS and Azure to MongoDB and PostgreSQL, now handles vector data. This consolidation raises two key questions: “Are standalone vector solutions still necessary?” or “Should teams continue with familiar multi-model systems like PostgreSQL?”

Deployment limitations add another critical dimension. For many data-heavy industries like IoT, manufacturing, and retail, there are rarely practical ways to run these databases where data actually lives. This constraint exposes a gap in edge and on-premises deployment support.

Additionally, AI agents are generating 10x more queries than human-driven applications, forcing a fundamental rethink of database throughput architecture. Despite the significance of these shifts, there is no thorough analysis of their implications for architectural decisions.

We examine the core forces that have transformed the vector database market, argue why specialized solution usage is declining, assess where edge deployment support stands in 2026, and present an actionable database decision framework that accounts for data you can't migrate to the cloud.

What Shifted in 2025

Pre-2025, purpose-built vector databases were presented as the standard infrastructure, but by 2026, a different reality emerges. Vectors have moved from being a database category to a data type.

Major traditional database providers, from PostgreSQL to Oracle and MongoDB, now add native vector support. MongoDB integrated Atlas Vector Search, PostgreSQL added pgvector and pgvectorscale extensions, and Oracle introduced Oracle Database 23ai. Top cloud providers, like AWS, Google, and Azure, also joined this trend.

Integrated vector support eliminates the need to introduce a separate database alongside your primary relational system to implement vector search for AI applications. While purpose-built vector databases still dominate vendor lists, the market has already moved on, and the PostgreSQL acquisitions make that clear.

In 2025 alone, Snowflake and Databricks spent approximately $1.25B acquiring PostgreSQL-first companies. At the same time, Stack Overflow reported PostgreSQL as the most used (46.5%) database among developers in 2025. These numbers signal that relational databases are now fit for AI workloads. But VentureBeat predicts that this shift will narrow down purpose-built platforms to specialized use cases.

By integrating vector search directly into production systems, traditional databases are compressing the role of dedicated vector infrastructure to billion-scale workloads with sub-50ms latency requirements, consistent with VentureBeat’s analysis and confirmed by PostgreSQL acquisitions.

To understand what this 2025 shift means for your architectural decisions in 2026, let’s first look at how we got here.

A Refresher on Vector Databases

Vector databases store, index, and query high-dimensional vector embeddings that represent multimodal data as numerical arrays to capture their semantic and contextual relationships. As unstructured data accounts for 90% of the global information footprint, encoding meaning for machine learning models requires embedding storage, vector search, and context retrieval, which vector databases handle. This infrastructure underpins many AI applications, including retrieval-augmented generation (RAG), recommendation systems, and natural language processing (NLP).

How Similarity Search Actually Works

The core retrieval technology for similarity search is approximate nearest neighbor search. Most databases use hierarchical navigable small world graphs (HNSW), inverted file (IVF), locality-sensitive hashing (LSH), or product quantization (PQ) ANN indexing algorithms.

When a query vector arrives, the database follows a graph, hash, or quantization-based approach to find approximate nearest neighbor candidates within the vector space. The database then computes the distance between these vectors, typically using cosine similarity or Euclidean distance functions to rank the top-K results, as illustrated in the image above. These ranked results either improve the context that becomes the final output or serve as a candidate set for re-ranking to identify more true nearest neighbors.

Why Retrieval-Augmented Generation (RAG) Made Vector Databases Essential

The persistent interest in vector databases is a direct response to large language models' hallucinations, lack of domain knowledge, and inability to incorporate up-to-date information into their responses, making them insufficient for accuracy-sensitive tasks. RAG methods augment LLM outputs, leveraging vector databases as external knowledge bases and vector search as the computational backbone for retrieving relevant context.

Conventional RAG systems build on a four-tier architecture: converting incoming queries into vector representations using an embedding model, executing a similarity search on stored vectors, integrating the retrieved relevant chunks and the query into an extended context that a language model processes, and finally transmitting the generated response back to the user.

Purpose-built vector databases simplified RAG implementation and efficient similarity search for early AI adopters. But three things changed between 2022 and 2025.

The Three Market Forces Reshaping Vector Databases in 2026

If 2022–2025 was about adding vector-native databases to AI applications, 2026 is leaning towards moving back to extended relational databases, rethinking architectural designs, and addressing an overlooked edge deployment gap. These three distinct trends stand out the most.

Force 1: Database Consolidation (Multimodal Platforms Win)

In 2026, major traditional relational databases have integrated vector capabilities into their data layer, and their extensions are already showing success with AI workloads. PostgreSQL’s pgvectorscale, for instance, benchmarked 471 QPS, against Qdrant's 41 QPS at 99% recall on 50M vectors. This consolidation means developers can now build moderate-scale production AI applications on general-purpose databases.

While purpose-built vector databases excel at vector search, infrastructure consolidation outweighs specialization when the workload doesn't demand it. Consider a product documentation knowledge base with 10M embedded documents, processing 500QPS, and requiring hybrid search. Traditional databases handle this workload effectively while also managing log collection, full-text search, and query analytics.

One relational database that stands out in 2026 is PostgreSQL. An optimized PostgreSQL database currently supports OpenAI's ChatGPT and API, and the reason is simple: PostgreSQL gives engineers the flexibility, stability, and cost control needed for GenAI development. There are fewer moving parts, the system combines transactional safety with analytical capability, and a familiar ecosystem anchors your stack.

Meanwhile, there's also the hybrid search advantage of PostgreSQL + pgvector that enables production systems to model nuanced relationships between data to match real user queries. Engineers prioritize databases that support personalization and enforce business rules such as price thresholds, categories, permissions, and date ranges. PostgreSQL achieves this richer data retrieval by merging dense and sparse vector embeddings. The database and its vector data extensions obtain query results from vector search, keyword matching, and metadata filters.

Below is a Python example that demonstrates vector similarity search with metadata filtering using PostgreSQL + pgvector. The code takes a pre-filtering approach, filtering rows first by price and category before measuring vector distance.

import psycopg2
import numpy as np
from pgvector.psycopg2 import register_vector

conn = psycopg2.connect("dbname=mydb user=postgres")
register_vector(conn)
cur = conn.cursor()

query_embedding = np.array([0.1, 0.2, 0.3])
min_price = 50
category = "electronics"

cur.execute("""
    SELECT product_name, price, category, embedding <-> %s AS distance
    FROM products
    WHERE price >= %s AND category = %s
    ORDER BY embedding <-> %s
    LIMIT 5
""", (query_embedding, min_price, category, query_embedding))

results = cur.fetchall()

for name, price, cat, dist in results:
    print(f"{name}: ${price} (similarity: {1-dist:.2f})")

Pure vector search focuses on only similarity search operations. In contrast, hybrid search provides a better basis for reasoning about interconnected information on diverse data types by capturing both semantic matches and contextually appropriate responses.

Vector-native solutions still matter, but for billion-scale use cases where performance, tuned indexes, and vector quantization are a priority. If you're building RAG applications or knowledge management systems, with a stable load of 50-100M vectors, traditional databases provide a unified platform where vectors and application data can reside in the same place.

Force 2: AI Agents Breaking the Query Model

AI agents are issuing 10x more queries than humans in 2026. This means the vector database infrastructure designed for human query patterns won't work for agents. Autonomous systems spin up an isolated PostgreSQL instance in <500ms, rely on heavy parallelism, and ingest large datasets continuously. Low-latency databases alone won’t serve this behavior. Throughput must also scale to match the surge in concurrency that agents will introduce in 2026.

However, not all vector databases are agent-ready, and optimizing for throughput often compromises latency. In production systems, these trade-offs become more pronounced.

Database providers must rethink their architectural designs to align with agentic workloads. Traditional caching strategies that focused solely on storing frequently accessed embeddings must evolve to leverage semantic cache, which reuses previously retrieved query-answer pairs under similar computing conditions. This setup can reduce latency and inference costs, while maintaining high throughput during high traffic.

At the indexing layer, databases must be configurable, exposing vector index parameters so engineers can tune trade-offs between speed, recall, and memory usage. To prevent server overload, databases must also move from static, reusable maximum connections to dynamic pool sizing that adjusts connection pools based on real-time demand. This minimizes running out of available connections under load or accumulating many idle ones.

In 2026, vector databases must rewire infrastructure design for an agentic era rather than waiting to be shaped by it.

Force 3: The Deployment Gap Nobody's Filling

While cloud databases have scaled to handle billions of vectors, developers building privacy-first, latency-sensitive applications at the edge are still being ignored in 2026.

The edge computing market was worth $168B in 2025, and IoT Analytics estimates the number of connected IoT devices will hit 39 billion by 2030. There's an active market, yet no one has filled the deployment gap.

What the market is ignoring is that cloud-only databases are not equipped for offline scenarios, with limited bandwidth and intermittent connectivity. Critical applications, such as in healthcare, demand real-time responses (<10ms) and continuous system availability. Inability to operate during outages can cost between $700 and $450,000 per hour, depending on the industry. Edge setup can provide that always-on infrastructure while cutting transit costs.

There are also the data security, compliance, and sovereignty requirements that regulated applications must meet by keeping data on-premises. Fulfilling these constraints means adapting infrastructure to support a secure, decentralized computing model that cloud systems cannot deliver. Edge deployment minimizes data movement and isolates sensitive workloads to reduce compliance scope.

For air-gapped environments, localized decision-making is non-negotiable. Public cloud deployments rely on persistent connections, but applications operating within a controlled perimeter must avoid outbound connections. Adopting a private cloud approach is costly and resource-intensive, whereas edge infrastructure succeeds by processing data locally at the source.

Yet in 2026, moving the edge beyond do-it-yourself setups is still in its early stages, despite a thriving market. Most hyperscalers currently treat edge computing as an extension of their existing cloud business. What the market needs is an edge-native solution that scales vertically to improve the network capacity, storage power, and processing ability of existing machines. But everyone still builds for the cloud.

These three forces reveal a market that needs careful architectural reevaluation. One might be taking a hybrid approach, combining cloud and on-premises deployment for edge use cases. Another option is returning to the Postgres environment we are already familiar with.

The PostgreSQL Renaissance (and What It Means)

Hyperscalers have been doubling down on PostgreSQL, and more engineers are choosing the database for enterprise-grade AI applications. This resurgence in interest and usage signals a change in infrastructure requirements for GenAI development.

Why the Hyperscalers Bet Big on PostgreSQL

Every hyperscaler has integrated PostgreSQL technology into its database services. Google offers Cloud SQL for PostgreSQL and AlloyDB, AWS has Amazon Aurora and Amazon RDS for PostgreSQL, and Microsoft provides Azure Database for PostgreSQL. Top data warehouse providers are not left out of this PostgreSQL adoption either.

In May 2025, Databricks acquired Neon for $1B. Snowflake followed the same trend in June 2025, acquiring Crunchy Data for an estimated $250M. In October 2025, Supabase also raised $100M in Series E funding.

Hyperscalers recognize PostgreSQL's familiar, versatile, and extensible infrastructure, which already powers many enterprise databases, and leverage it to support engineers building agentic AI applications with PostgreSQL compatibility. With a 40-year market run, the open-source vector database has developed a mature tooling, flexible enough for both online transaction processing (OLTP) and AI application development. Plus, its dual JSON and vector support enables teams to build on the foundation they already know and scale from it.

At the same time, PostgreSQL’s pgvector and pgvectorscale extensions, with HNSW and StreamingDiskANN indexes, mean vector storage and similarity search happen directly within the database.

Another factor fueling the PostgreSQL comeback is its ACID-compliant engine. Hyperscalers work with enterprise teams seeking data integrity and application stability for critical systems such as financial applications. PostgreSQL's transactional guarantees offer predictable and consistent behavior for production workloads.

Despite hyperscalers’ convergence on PostgreSQL, AWS has presented a counter-trend to its PostgreSQL-based offerings with S3 Vectors. Instead of indexing vectors inside a database, embeddings live in object storage, querying 2 billion vectors per index. AWS positions this storage-first model as a 90% TCO reduction for AI workloads, trading low latency (>100ms) for cost efficiency. This S3 Vectors’ deviation highlights PostgreSQL's scale limits.

PostgreSQL is fast enough for many vector data workloads, but specialized architectures still win at scale. For instance, PostgreSQL’s multiversion concurrency control (MVCC) implementation is inefficient for write-heavy workloads, like real-time chat systems. During high write traffic, tables bloat and indexes require more maintenance, which in turn degrades application performance.

When PostgreSQL with pgvector Is Enough

If your application already relies on PostgreSQL, introducing pgvector is a natural extension rather than adopting a new infrastructure or performing costly data migrations. Your vectors live next to your relational data, and you can query them in the same transaction using both similarity search and SQL JOINs. This hybrid search capability improves your application's retrieval layer and data management beyond pure vector search, with metadata constraints.

PostgreSQL + pgvector also performs well for moderate-scale vector operations such as enterprise knowledge bases or internal RAG applications, where you're handling <100M vectors, with sub-100ms latency requirements.

When You Still Need Purpose-built

If vector search is your primary workload, purpose-built platforms offer indexing structures, high-precision similarity search, and low-latency execution paths tuned for billion-scale vectors and high-throughput applications like recommendation or search engines. Dedicated databases are also effective if your search requirements demand specific capabilities like an HNSW index with dynamic edge pruning or sub-vector product quantization.

This table summarizes the key differentiators between purpose-built databases and PostgreSQL + pgvector extension.

Features	Purpose-built	PostgreSQL + pgvector
Performance (QPS)	>5k QPS	500–1500 QPS
Scale (max vectors)	Billions of vectors	<100M
Latency	<50 ms	<100 ms
Cost model	Usage-based for cloud-native databases; infrastructure-driven for self-hosted	Infrastructure-driven
Operational complexity	Fully managed for cloud-based databases; self-hosted options require infrastructure ownership	Requires proficiency in SQL and PostgreSQL-specific features
Developer experience	Designed for speed and abstraction; provides APIs and SDKs	Broad tooling support with many connectors and libraries for different development use cases

One key factor driving teams to rethink database choices in 2026 is cost. Cloud-based vector databases like Pinecone reveal something uncomfortable about cloud bills.

Cloud Economics Are Breaking (Usage-Based Pricing at Scale)

Usage-based pricing seems cost-effective for modest workloads until a system succeeds. Consider a RAG application handling 10M queries per month. At first, the base storage and computational cost feel predictable. But as traffic grows to 150M, the cumulative costs of storage, database lookups, indexing recomputation, and egress fees reveal how volatile usage-based billing becomes at scale.

For instance, with 100M (1024-dim) vectors, 150M queries, and 10M writes per month, your estimated Pinecone bill for the RAG application will total around $5,000-$6,000, accounting only for storage, query cost, and write cost. If you factor in egress fees of about $0.08 per GB, the bill escalates further when data transfer is involved.

Teams using cloud-based vector databases have reported surprise bills up to $5,000 on Reddit. Market pricing trends also echo this cloud bill volatility. In 2025, cloud vendors introduced price hikes estimated at 9-25%, and between 2010 and 2024, cloud database costs increased by 30%, with usage-based pricing becoming the dominant model.

In cloud environments, costs scale unpredictably with growing data volume and query frequency. Pay-as-you-go pricing is the accelerant here, amplifying unreliable cost forecasting. Meanwhile, cloud vendors’ incentives scale with your consumption. More queries, storage, and processing result in higher, unpredictable bills for teams, while vendor revenue grows. Deloitte reported that companies adopting usage-based models grow revenue 38% faster year-over-year.

Consumption-driven billing promises automatic scaling with workload demand. But teams often lack visibility into exactly what drives the spend and receive bills for both active queries, idle replicas, redundant embedding recomputation, and cloud add-ons. With the variability of the usage-based pricing model, it makes sense to reassess deployment strategy.

For workloads with predictable traffic, teams can trade the flexibility of a usage-based model for the cost stability of reserved capacity. For instance, committing to a one-year reserved capacity plan can reduce the cost of handling 150M queries per month to $40,000-$42,000 annually, about 32% less than the usage-based pricing cost.

Migrating to on-premises infrastructure is another alternative for teams with existing DevOps maturity. There's the upfront hardware and security investments. But when optimized, on-premises deployment can significantly control cost. For instance, a self-hosted Milvus deployment handling 150M vectors might require three m5.2xlarge instances plus distributed storage, totaling around $900-$1,000 per month.

For latency-critical workloads, edge processing provides another path. Processing 5TB of data at the edge, for example, can save approximately $400-$600 in egress fees. But there's still a huge gap in edge deployment.

The Edge Deployment Gap (Where the Market Isn't Looking)

Market attention has focused on cloud vector databases, but they don’t tell the full story of what is happening in offline and air-gapped environments where security, ultra-low latency, decentralization, and compliance are non-negotiables.

In 2026, more enterprises are leaning towards edge deployment, indicating a rethink of how teams want to handle data processing. Regulated industries need infrastructure that runs where most data decisions are already made, on devices at the network’s edge. Edge deployment meets this demand by keeping computation closer to the source.

Gartner projects that 55% of deep neural network data analysis will occur at the edge. Yet the edge AI ecosystem remains immature. Cloud is not dead, but there are mission-critical workloads today that cloud deployment cannot support efficiently.

Use Cases Cloud Vendors Can't Address

While cloud vendors offer mature features for integrating vector search into enterprise workflows, there are still use cases they aren't equipped to handle:

Healthcare: Medical data and patient records often reside on-premises, governed by HIPAA, GDPR, and other privacy regulations. Hospitals need real-time health analysis happening on-premises, as migrating private data to the cloud expands their attack surface, requires a strong security posture, and increases compliance overhead.
Autonomous systems: Autonomous vehicles need split-second local decision-making on camera and LiDAR data to maintain situational awareness, with or without external connectivity. Network round-trips to cloud servers limit the delivery of this time-sensitive data.
Military: Military services manage sensitive assets through classified networks in an air-gapped and high-risk environment. They expect to push an update to an edge node and have it go live across the fleet in real time for tactical operations. Military services cannot tolerate the network latency and bandwidth constraints of the public cloud.
Manufacturing: Manufacturing sites’ network carries real-time sensor streams, safety systems, and production telemetry that require immediate analysis for predictive maintenance and operational efficiency. Some manufacturing facilities operate in remote locations with no connectivity, so going "cloud-first” is impractical, as they need solutions designed for interference-heavy factory floors.
Retail: Retail businesses need consistent local retrieval and immediate analysis of point-of-sale data, regardless of intermittent connectivity, as downtime costs approximately $700 per hour.

These use cases show where cloud vector databases still struggle to meet the latency and security requirements of on-device data. What features enable edge vector databases to satisfy these requirements, and why are comprehensive solutions still scarce?

What an Edge Vector Database Needs

Edge vector databases run on edge servers, enabling AI applications to process data stored locally and receive responses in real time without waiting for back-and-forth communication with the cloud.

Unlike cloud environments, which assume steady connectivity and large compute power, edge solutions are engineered to manage unstable networks and process local data under resource constraints. With edge vector databases, data stays at its point of generation, ingestion and analysis happen in real time, and the system adapts to unpredictable conditions at the edge.

There are three core design requirements an edge database needs to deliver on this promise of speed and reliability:

Lightweight infrastructure: Distributed operations require infrastructure that is lightweight and deployable by design for resource-constrained edge servers. Having a compact in-memory data structure also helps to minimize the database memory footprint.
Offline capability: Edge databases must execute local data analytics without relying on connected servers. Even with intermittent connectivity and limited bandwidth, AI applications should remain functional and operate independently.
Sync-when-connected architecture: Edge databases must automatically sync offline data, resolve conflicts, and reflect data changes when connectivity is restored. This mechanism helps to track performance metrics locally and maintain operational visibility.

Despite growing demand, the database market has few edge-native solutions because designing one that ticks the lightweight, offline-capable, and synchronization boxes is complex.

Why Nobody's Building This

The edge deployment model remains an underdeveloped market with fragmented tooling for several reasons.

One, edge infrastructure is complex, emphasizing fault tolerance and near-instant latency. Teams also need immediate visibility into device status, synchronization health, and data integrity across potentially thousands of endpoints. But edge devices, such as sensors and cameras, have limited compute and memory resources.

Even enterprise-level control hosts often cap at 2-16GB of memory, significantly smaller than the memory centralized servers provide. Running inference on these devices will waste resources at their edge nodes and increase latency. Optimizing for real-time results becomes harder.

However, that hardware baseline is improving. Advancements in edge computing, including the adoption of Ampere architecture, and the increasing prevalence of devices like the Jetson Nano, are expanding the amount of usable compute available at the edge.

Another challenge is that edge computing is inherently distributed, with configurations varying across several hardware that operate independently. This hardware heterogeneity complicates data synchronization between diverse edge devices, especially as workloads shift across an unpredictable network.

Nobody is building edge deployment models because of the operational complexity and specialization they require. Purpose-built databases like Qdrant add edge computing support, but still primarily operate under a centralized model. Edge-specific databases barely exist, with ObjectBox being a rare exception. The vendors who get it right must find a balance between strict latency requirements, hardware orchestration, consistent operational performance, and computational power.

This table highlights where each available database deployment strategy thrives and where it falls short.

Deployment model	Pros	Cons	Best for
Cloud-native	Ready-to-use solution, faster time-to-success, auto-scaling	High TCO at scale, cyberattack vulnerability, and increased latency with each network hop	Teams seeking managed infrastructure
On-premises	Development flexibility, full control and customization, data privacy	High upfront fees, maintenance burden	Organizations in regulated sectors with stringent data privacy requirements
Edge/offline	Near-instant latency, local data processing	Emerging market, lacks infrastructure software	Engineers building latency-critical AI applications or seeking decentralized data processing
Hybrid	Keeps control systems local while leveraging cloud analytics	Management complexity, high latency	Organizations seeking both cloud scalability and on-prem flexibility and security

Engineers can explore a hybrid approach that combines cloud for elasticity, on-premises for flexibility, and edge for speed.

What To Do in 2026 (Decision Framework)

The decision you make in 2026 can mean the difference between an AI application that thrives and one that struggles. Your architecture evaluation should prioritize your performance goals, scale, preferred cost model, existing stack, regulatory requirements, and data sovereignty needs.

If You're Starting Fresh

Workload patterns should be your decision driver, not industry trends or scale panic. Is your AI application handling:

<10M vectors: Start with PostgreSQL + pgvector, especially if your core data already lives in PostgreSQL. pgvector thrives with moderate data scale, and its hybrid search architecture improves retrieval quality for RAG applications.
10M-100M vectors: Both purpose-built databases and PostgreSQL's pgvectorscale can serve your workload, but with trade-offs. PostgreSQL + pgvectorscale works effectively at this scale, but performance might degrade with dynamic workloads or concurrent queries. Purpose-built outperforms in auto-scaling with increased data volume, and in maintaining persistent latency during traffic spikes. The trade-off is unpredictable cloud costs or operational overhead for self-hosted solutions.
100M+ vectors: Use specialized vector databases like Pinecone, Qdrant, and Milvus. They are designed for billion-scale vector operations, especially for high-throughput vector search (> 1,000 QPS) and high concurrent writes.

However, if your application must run offline, the options on the market are still limited.

If You're Already Using a Vector Database

Architect for expansion, but analyze your present situation. You should:

Evaluate cost trajectory: Track your actual monthly spend, considering factors like data volume, QPS requirements, storage, and computation. At your projected growth, deduce what your current bill will look like in 12 months. If the numbers demand a more predictable cost model, consider reserved capacity or on-premises deployment. But if usage-based pricing better aligns with your budget and scale, continue with it.
Benchmark query patterns: Determine the dataset size your application processes monthly, and its average query latency. If you're hitting agent-scale queries, consider implementing optimization methods like semantic caching and quantization, or horizontal scaling techniques like sharding, which partitions agent memory, embeddings, and tool state, enabling parallel writes. For fluctuating workloads, future-proofing your vector database means designing for elastic scaling, which cloud solutions can provide.
Consider PostgreSQL migration if scale permits: If growth is slow (for instance, 10M vectors, 200 QPS average, doubling every 6-12 months), migrating to PostgreSQL fits this scenario.
Assess deployment model constraints: Understand the strengths and limitations of your current runtime environment. Cloud vendors introduce non-linear costs and compliance overhead. On-premises setup presents high upfront expenses and limited elasticity. Edge deployment means limited resources and synchronization complexity. Being realistic about these constraints helps you validate that switching vector databases solves a real problem rather than creating new ones.

If You Need Edge/On-premises

Understand that while cloud vendors compete for hyperscale workloads, edge deployment remains largely unaddressed. As a result:

Evaluate rare options: Native edge deployment solutions are scarce, but some existing options include ObjectBox, an on-device NoSQL object database, and pgEdge, an extension of standard PostgreSQL, but for distributed setups. There are also industry-specific custom edge solutions, but each comes with trade-offs in maturity, scalability, or ecosystem support.
Consider using PostgreSQL on-premises with pgvector: If you already have operational capacity, deploying PostgreSQL on-premises gives you total control over your database environment. The trade-off is manually optimizing for performance, monitoring, and security.
Anticipate new market entrants: The native edge deployment gap discussed earlier remains largely overlooked by major vendors, but emerging solutions, such as Actian VectorAI DB, are addressing this gap with a database that accounts for the physical and network realities of offline scenarios. Specifically, Actian supports local data analytics in environments with unstable connectivity, such as store checkout hardware and factory-floor machinery.

The flowchart below captures this decision framework at a glance.

The Bottom Line

This analysis has spotlighted fundamental shifts in a market that focused squarely on purpose-built vector databases before 2025.

In 2026, vectors are now a data type, and we are seeing more teams returning to the relational databases where their data already lives and leveraging their vector extensions. PostgreSQL is at the forefront of this renewed interest, providing the ACID-compliance, operational expertise, and flexibility that GenAI applications need. What this means for purpose-built solutions is that they now matter only for high-throughput, recall-sensitive systems.

Meanwhile, even for high-throughput vector databases, AI agents’ query pressure is forcing a rethink of architectural design to support parallel writes and concurrent requests at a new scale. On top of this, fragmentation defines edge and on-premises deployments, with few straightforward approaches for processing data closer to the point of production.

Looking ahead, the next shift will come from vendors that move beyond 2024's cloud-first database promotions to cater to the growing demand for offline-capable architecture. If you need to run AI workloads on-premises or at the edge, the options in 2026 are still limited, but that gap is starting to close with databases like Actian VectorAI DB. Join the waitlist for early access.