DEV Community: Lina Lam

The Complete Guide to LLM Observability Platforms in 2025

Lina Lam — Thu, 15 May 2025 16:00:00 +0000

Building production-grade AI applications requires more than just crafting the perfect prompt. As your LLM applications scale, monitoring, debugging, and optimizing them become essential.

This is where LLM observability platforms come in.

But with so many options available, which one should you choose? This guide compares the best LLM monitoring tools to help you make an informed decision.

Introduction to LLM Observability Platforms
Key Evaluation Criteria for LLM Observability Tools
Types of LLM Observability Solutions
Comparing Top LLM Observability Tools
Detailed Feature Comparison
Comparing Helicone vs. Alternatives
How to Choose: Decision Framework
Conclusion

Introduction to LLM Observability Platforms

LLM observability platforms are tools that provide insights into how your AI applications are performing. They help you track costs, latency, token usage, and provide tools for debugging workflow issues. When we discuss LLM observability, it encompasses aspects like prompt engineering, LLM tracing, and evaluating the LLM outputs.

As LLMs become increasingly central to production applications, these tools have evolved from nice-to-haves to mission-critical infrastructure.

The right observability platform can:

Reduce operating costs through caching and optimization
Improve reliability by catching errors before users do
Enhance performance by identifying bottlenecks
Support collaboration between teams working on LLM applications
Enable data-driven decisions about prompt engineering and model selection

Key Evaluation Criteria for LLM Observability Tools

When choosing an LLM observability platform, consider these critical factors:

1. Implementation & Time-to-Value

Ease of integration: How quickly can you get started?
Integration methods: Proxy-based, SDK-based, or both?
Supported providers: Which LLM providers and frameworks are supported?

2. Feature Completeness

Monitoring features: Request logging, cost tracking, latency monitoring, AI agent observability, user tracking etc.
Evaluation & debugging: LLM tracing tools, session visualization, prompt testing, scoring, etc.
Optimization: Caching, Gateways, prompt versioning, experiment, etc.
Security: API key management, rate limiting, threat detection, self-hosting, etc.

3. Technical Considerations

Scalability: Can the platform handle your traffic volume?
Self-hosting options: Can you deploy it on your infrastructure?
Data privacy: How is your data protected?
Latency impact: How much overhead does it add?

4. Business Factors

Pricing model: Per-seat, per-request, or hybrid?
ROI timeline: How quickly does it pay for itself?
Support quality: How quickly can you get support?
Product roadmap: What pace are features being added? Do they align with your needs?

Types of LLM Observability Solutions

The market for LLM observability has evolved into distinct categories. Here's what you need to know:

Category	Examples	Pros	Cons
LLM-specific observability platforms	Helicone, LangSmith, Langfuse	• Purpose-built for LLM workflows • Deep integration with LLM providers • Specialized features for prompt management	• May lack broader application monitoring capabilities • Newer platforms with evolving feature sets
General AI observability platforms	Arize Phoenix, Weights & Biases, Comet	• Support for both traditional ML and LLMs • More mature evaluation capabilities • Broader ecosystem integration	• Less specialized for LLM-specific workflows • Often more complex to set up
LLM gateways with observability	Portkey, OpenRouter, Helicone	• Combined routing and observability • Model fallback capabilities • Provider-agnostic	• May prioritize routing over deep observability • Often less robust analytics

Comparing Top LLM Observability Tools

At a Glance

Below is a quick comparison of the major competitors in the LLM observability space:

Feature	Helicone	LangSmith	Langfuse	Braintrust	Arize Phoenix	HoneyHive	Traceloop	Portkey	Galileo	W&B
Open-source	✅	❌	✅	🟠 (only the AI proxy)	✅	❌	✅	✅	❌	❌
Integration method	Proxy, or SDK	SDK	SDK (primarily)	SDK	SDK	SDK	SDK	Proxy + SDK	SDK	SDK
Self-hosting	✅	✅ (Enterprise plan only)	✅	✅	❌	❌	✅	✅	✅ (Enterprise)	❌
Cost tracking	Advanced	Basic	Basic	Basic	Basic	Basic	Limited	Advanced	Basic	Basic
Caching	✅	❌	❌	✅	❌	❌	❌	✅	❌	❌
Prompt management	✅	✅	✅	✅	✅	✅	✅	✅	✅	✅
Built-in security	✅	❌	❌	❌	❌	❌	❌	✅	✅	❌
Evaluation	Basic	Advanced	Basic	Advanced	Advanced	Advanced	Basic	Basic	Advanced	Basic
Multi-modal tracing	✅	✅	✅	❌	✅	✅	❌	❌	❌	✅
Best for	Fastest integration, LLM provider agnostic	LangChain workflows	Complex tracing	Evaluation-first approach	Model quality analytics	Human-in-the-loop evaluation	OpenTelemetry-based observability	Routing & gateway capabilities	Enterprise evaluation	ML ecosystem users

💡 What makes Helicone different?

Helicone is designed for the fastest time-to-value and easiest to get started with. While other platforms may require days of integration work, Helicone can be implemented in minutes with a single line change to your base URL.

Teams choose Helicone when they need comprehensive observability with minimal engineering investment and want features that directly impact the bottom line, like built-in caching that can reduce API costs by 20-30%.

Detailed Feature Comparison

Let's dive deeper into how these platforms compare.

Helicone: The Developer-First LLM Observability Platform

Helicone is an open-source AI observability platform designed to help teams monitor, debug, and optimize their AI applications with minimal setup. Unlike solutions that require extensive SDK integration, Helicone can be implemented with a simple URL change in most cases.

Key Differentiators

One-Line Integration: Get started in under 30 minutes by simply changing your API base URL. Here's an example of using Helicone with OpenAI:

  client = OpenAI(
    api_key="your-api-key-here", 
    base_url="https://oai.helicone.ai/v1",  # Change your base URL
    default_headers= {  
      "Helicone-Auth": f"Bearer {HELICONE_API_KEY}", # add this header
    }
  )

Cost Monitoring & Optimization: API costs are calculated automatically as requests are sent. Using built-in caching can reduce API costs by 20-30%.

  # Enable caching with a simple header
  client.chat.completions.create(
      model="text-davinci-003",
      prompt="How do I cache with helicone?",
      extra_headers={
        "Helicone-Cache-Enabled": "true", 
      }
  )

Comprehensive Analytics: Track token usage, latency, and costs across users and features. View all your data in a single dashboard.
AI Agent Observability: Visualize complex multi-step AI workflows with session tracing. Pinpoint the exact step that failed.
Advanced Gateway Capabilities: Route between different LLM providers with failover support.
Self-Hosting: Deploy on your infrastructure with Docker, Kubernetes, or manual setup.

"Probably the most impactful one-line change I've seen applied to our codebase."

— Nishant Shukla, Senior Director of AI, QA Wolf

Architectural Advantage

Helicone's distributed architecture (using Cloudflare Workers, ClickHouse, and Kafka) is designed for high scalability, having processed over 2 billion LLM interactions. The platform adds an average latency of only 50-80ms.

This architecture enables Helicone to support both cloud usage and self-hosting, with straightforward deployment options via Docker, Kubernetes, or manual setup.

Comparing Helicone vs. Alternatives

1. Helicone vs. LangSmith

LangSmith, developed by the team behind LangChain, excels at tracing complex LangChain workflows.

Key differences:

Helicone offers proxy-based integration; LangSmith requires SDK integration.
Helicone is fully open-source; LangSmith is proprietary.
Helicone provides built-in caching; LangSmith does not (though LangChain does).
LangSmith has deeper LangChain integration.

Read full comparison: Helicone vs LangSmith

💡 Bottom Line

Helicone is best for rapid implementation and cost reduction. LangSmith is great for deep LangChain integration.

2. Helicone vs. Langfuse

Langfuse is another open-source observability platform with a strong focus on LLM tracing.

Key differences:

Helicone uses a distributed architecture (ClickHouse, Kafka); Langfuse uses a centralized PostgreSQL database.
Helicone offers proxy-based integration; Langfuse is SDK-based.
Helicone has built-in caching; Langfuse does not.
Langfuse has more detailed tracing for complex workflows.

Read full comparison: Helicone vs Langfuse

3. Helicone vs. Braintrust

Braintrust focuses on LLM evaluation with an emphasis on enterprise use cases.

Key differences:

Helicone provides comprehensive observability; Braintrust specializes in evaluation.
Helicone offers a one-line proxy integration; Braintrust requires SDK integration.
Helicone has more extensive observability features; Braintrust excels at advanced evaluations.
Helicone provides flexible pricing; Braintrust is enterprise-focused.

Read full comparison: Helicone vs Braintrust

4. Helicone vs. Arize Phoenix

Arize Phoenix focuses on evaluation and model performance monitoring.

Key differences:

Helicone supports self-hosting; Arize Phoenix does not.
Helicone provides comprehensive observability features; Arize focuses on evaluation metrics.
Helicone has better cost-tracking features.
Helicone offers one-line integration; Arize requires more setup.
Arize provides stronger evaluation capabilities; Helicone offers more operational metrics.

Read full comparison: Helicone vs Arize Phoenix

5. Helicone vs. HoneyHive

HoneyHive specializes in human-in-the-loop evaluation of LLM outputs.

Key differences:

Helicone is open-source; HoneyHive is proprietary.
Helicone provides built-in caching; HoneyHive does not.
Helicone focuses more on observability; HoneyHive focuses on evaluation.
HoneyHive has stronger tools for human evaluation; Helicone focuses on automated metrics.

Read full comparison: Helicone vs HoneyHive

6. Helicone vs. Traceloop (OpenLLMetry)

Traceloop provides observability through OpenTelemetry standards.

Key differences:

Helicone offers proxy-based integration; Traceloop is SDK-based.
Helicone provides built-in caching and cost optimization; Traceloop does not.
Helicone has more comprehensive security features; Traceloop has stronger OpenTelemetry integration.
Helicone has a more user-friendly UI; Traceloop is more developer-focused.

Read full comparison: Helicone vs Traceloop

7. Helicone vs. Galileo

Galileo specializes in evaluation intelligence and LLM guardrails.

Key differences:

Helicone is open-source; Galileo is proprietary.
Helicone offers proxy-based integration; Galileo requires SDK integration.
Helicone provides built-in caching; Galileo does not.
Galileo excels at evaluation metrics and guardrails; Helicone offers more comprehensive observability.
Helicone has more flexible pricing; Galileo is enterprise-focused.

Read full comparison: Helicone vs Galileo

8. Helicone vs. Weights & Biases

Weights & Biases is a mature ML platform that has expanded to support LLMs.

Key differences:

Helicone is purpose-built for LLMs; W&B is broad ML infrastructure.
Helicone offers simple integration; W&B requires more setup.
Helicone has specialized LLM features; W&B has stronger experiment tracking.
Helicone provides more accessible pricing; W&B can become expensive at scale.

Read full comparison: Helicone vs Weights & Biases

9. Helicone vs. Portkey

Portkey is an LLM gateway that includes observability features.

Key differences:

Helicone focuses on observability; Portkey emphasizes routing.
Helicone provides more detailed analytics; Portkey offers stronger failover capabilities.
Helicone has a more intuitive UI; Portkey has richer prompt management.
Both offer caching and routing capabilities.

Read full comparison: Helicone vs Portkey

10. Helicone vs. Comet

Comet provides comprehensive ML experiment tracking with LLM features.

Key differences:

Helicone is specialized for LLM observability; Comet covers broader ML tracking.
Helicone offers one-line integration; Comet requires more code changes.
Helicone provides built-in caching; Comet focuses on evaluation.
Comet has stronger evaluation automation; Helicone offers more operational insights.

Read full comparison: Helicone vs Comet

11. Building Your Own Observability Solution

If you're looking for a more custom solution, you can build your own observability solution in-house.

Our analysis shows that while building basic LLM request logging might take just 1-2 weeks, developing a fully-featured observability system with caching, advanced analytics, and proper scaling requires 6-12 months of engineering time, plus ongoing maintenance.

This decision involves factors like:

Development resources: Can you allocate engineering time away from your core product?
Maintenance burden: Are you prepared to maintain and update an internal tool?
Feature completeness: Can your custom solution match specialized platforms?
Time-to-value: How quickly do you need observability capabilities?

For a comprehensive breakdown of this build vs. buy observability decision, read our in-depth guide.

See the Helicone difference for yourself

Try Helicone for free and compare it against your current observability solution. Get started in minutes with one line of code.

Get a Free Trial 🔥

How to Choose: Decision Framework

Choosing the right observability platform depends on your specific needs and constraints. Use this decision framework to guide your selection:

Platform	Choose if you:
Helicone	- Need minimal integration effort (one-line setup) - Want comprehensive observability with cost optimization - Require easy-to-set-up self-hosting - Need support for multiple LLM providers - Want both technical and business analytics in one platform - Need routing capabilities between different LLM providers
LangSmith	- Are heavily invested in the LangChain ecosystem - Need deep tracing for complex LangChain workflows - Prefer an SDK-based approach with detailed function-level tracing
Langfuse	- Prefer open-source with simple self-hosting - Need detailed tracing for complex workflows - Are comfortable with an SDK-based approach - Want flexible community support
Braintrust	- Focus primarily on LLM evaluation - Need enterprise-grade evaluation tools - Want specialized test case management - Need to implement advanced prompt iteration capabilities - Want CI/CD integration for LLM testing
Arize Phoenix	- Focus more on LLM evaluation than operational metrics - Need advanced evaluation metrics for model quality - Are less concerned with cost tracking - Want integration with broader ML observability
HoneyHive	- Prioritize human evaluation of LLM outputs - Need detailed annotation workflows - Are less focused on operational metrics - Want specialized testing capabilities
Traceloop	- Need OpenTelemetry-based observability - Want code-first observability tools - Need a standardized approach to LLM monitoring - Want to integrate with existing OpenTelemetry systems
Portkey	- Need advanced routing and gateway capabilities - Want model failover and load balancing - Need virtual API key management - Require modular prompt management with "prompt partials"
Galileo	- Need enterprise-grade evaluation metrics - Want built-in LLM guardrails - Need quality assessment tools - Are less concerned with cost optimization features
Weights & Biases	- Need integrated ML experiment tracking - Already use W&B for traditional ML models - Want visualization tools for LLM experiments - Need broader ML lifecycle management

💡 Implementation Tip

Start with a proof of concept (POC) on a single application or component of your application. This allows you to measure real impact before scaling to your entire organization. With platforms like Helicone that offer one-line integration, you can typically complete a POC in under a day.

Try Helicone for Free

Conclusion

The right AI monitoring platform can significantly improve your AI application's performance, reliability, and cost-efficiency. While each platform has its strengths, Helicone's combination of ease of use, comprehensive features, and flexible deployment options makes it a strong choice for most teams.

Ultimately, your choice should be guided by your specific requirements, team structure, and existing tech stack. Consider starting with a free trial of multiple platforms to find the best fit for your needs.

How to Track LLM User Feedback to Improve Your AI Applications

Lina Lam — Wed, 14 May 2025 16:00:00 +0000

In today's AI-driven landscape, learning how to effectively track LLM user feedback is crucial for improving performance and driving higher user satisfaction.

Every user interaction provides valuable insights that can help you refine your AI's responses to better serve your customers' needs.

In this article, we will show you how to use LLM feedback tracking tools like Helicone to collect, analyze, and implement user feedback for continuous improvement of your AI applications.

Why is Tracking User Feedback Critical for LLM Applications?
The Feedback Collection Framework
Turning User Feedback into Training Datasets
Implementation Best Practices
Useful Resources

Why is Tracking User Feedback Critical for LLM Applications?

Creating a continuous user feedback loop is essential for any successful software application. This applies to LLM applications as well.

Collecting LLM user feedback creates a virtuous cycle of improvement through five critical stages:

User Interaction - Users engage with your LLM application
Feedback Collection - You gather structured data on response quality.
Pattern Analysis - You identify trends and opportunities for improvement
Dataset Creation - You create specialized training datasets based on feedback
Prompt Optimization - You fine-tune your models or update your prompts accordingly

This systematic approach is useful for building better AI products while reducing costs associated with poor user experiences.

A study published by Google DeepMind in April 2024 showed that aligning LLM outputs with user feedback led to a significant increase in positive user interactions, as evidenced by a larger positive playback rate gain.

Other studies also showed that incorporating user feedback into LLM application development leads to more efficient customer service operations. For example, Gorgias reported a 52% faster resolution of support tickets. Meanwhile, KPMG's Global CEE Report 2023-24 reported a 30% reduction in operational costs.

The Feedback Collection Framework

Helicone, an open-source observability platform for LLM applications, provides several powerful methods to gather, organize, and analyze user feedback for your LLM applications.

Method 1: Implementing the Feedback API

The most direct way to log user feedback is through Helicone's dedicated Feedback API:

import requests

# First, make your LLM call
response = client.chat.completions.create(
    model="gpt-4",
    messages=[{"role": "user", "content": "Write a short poem about AI"}],
    extra_headers={
        "Helicone-Auth": f"Bearer {HELICONE_API_KEY}"
    }
)

# Get the Helicone request ID
helicone_id = response.response.headers.get("helicone-id")

# Log user feedback (true for positive, false for negative)

feedback_url = f"https://api.helicone.ai/v1/request/{helicone_id}/feedback"
headers = {
    "Helicone-Auth": f"Bearer {HELICONE_API_KEY}",
    "Content-Type": "application/json"
}
data = {
    "rating": True  # True for positive, False for negative
}

requests.post(feedback_url, headers=headers, json=data)

This approach allows you to capture binary feedback (positive/negative) that's directly tied to specific LLM interactions, creating a clear connection between user sentiment and actual LLM responses.

Method 2: Using Custom Properties

For more nuanced feedback collection, Helicone's custom properties allows you to attach custom metadata to your LLM requests.

Simply add a Helicone auth-header, then a header for each custom property you want to track:

client.chat.completions.create(
    model="gpt-4",
    messages=[{"role": "user", "content": "Write a product description for a coffee maker"}],
    extra_headers={
        "Helicone-Auth": f"Bearer {HELICONE_API_KEY}",
        "Helicone-Property-Feedback-Rating": "4",  # On a scale of 1-5
        "Helicone-Property-Feedback-Comment": "Good but too lengthy",
        "Helicone-Property-User-Type": "content-marketer"
    }
)

Custom properties help you:

Capture numeric ratings beyond binary feedback
Include qualitative feedback comments
Segment feedback by user types, features, or use cases
Track performance across different environments (development, staging, production)

Method 3: Advanced User Metrics Tracking

To go one step further, you can monitor your users' interactions with your AI models to gain deeper insights into usage patterns and their satisfaction levels.

Tracking user metrics in Helicone is similar to tracking custom properties. Simply add a Helicone auth-header (if you haven't already), then the header helicone-user-id: <user_id> for the user you want to track.

client.chat.completions.create(
    model="gpt-4",
    messages=[{"role": "user", "content": "Summarize this article about AI trends"}],
    extra_headers={
        "Helicone-Auth": f"Bearer {HELICONE_API_KEY}",
        "Helicone-User-Id": "user_12345"  # Associate request with specific user
    }
)

By tracking user metrics, you can:

Analyze per-user request volumes and frequencies
Track costs associated with individual users
Identify power users and their behavior patterns
Detect usage anomalies that might indicate problems
Correlate feedback with usage intensity

This user-level data provides you with the context and granularity to interpret the feedback collected and prioritize improvements that benefit your most valuable users.

Pro Tip 💡

By combining multiple custom properties, you can create a rich feedback dataset. For example, setting up three custom properties - user role, feature used, and satisfaction rating - gives you powerful insights into which features work best for different user segments.

Turning User Feedback into Training Datasets

Once you've collected sufficient feedback, you now have valuable training data for improving your LLM applications. Let's look at how:

Step 1: Filtering and Exporting Your Feedback Data

First, filter your LLM request data based on factors such as:

Positive vs. negative feedback
Specific feature usage
User segments
Time periods

This specialized dataset represents real-world interactions, which can now be exported from Helicone's dashboard or API to drive meaningful, targeted improvements.

Step 2: Identifying Actionable Insights

With data in hand, analyze your feedback data to identify:

Common pain points or issues associated with negative feedback
Highly successful interactions from positive feedback
How performance varies across different user segments
Feature-specific feedback patterns

The goal is to discover actionable insights that can guide very specific optimization efforts.

Step 3: Creating Specialized Training Datasets

Based on your analysis, create specialized datasets tailored to your specific improvement goals.

Here's a video on how to create a dataset using Helicone's UI - you can also create a dataset programmatically for more advanced use cases.

Your browser does not support the video tag.

Success Stories

Journalist AI: Subscription-Based Feedback Segmentation

Journalist AI is a platform that automates content creation for writers. They use Custom Properties to segment feedback by subscription plan.

Their feedback collection strategy helps them:

Compare content satisfaction between free and paid users
Identify which features drive paid subscriptions
Track costs-to-value ratio for different user tiers
Target marketing efforts for high-value features

This approach has allowed them to increase their premium conversion rate by 22% in just three months.

Greptile: Repository-Specific Performance Tracking

Greptile helps users search and analyze text data from various sources. They use custom properties to track feedback by repository:

This strategic approach allows them to:

Measure satisfaction with results from different data sources
Track performance metrics (latency, costs) by repository
Identify which repositories need quality improvements
Understand user search patterns across data sources

Since implementing repository-specific tracking, they've been able to optimize their system for specific data sources, improving both response quality and speed.

Implementation Best Practices

To maximize the value of your feedback collection:

Be consistent with property naming - Use standardized naming conventions for Custom Properties
Collect feedback at the right time - Ask for feedback immediately after users interact with AI responses when the experience is fresh
Keep the feedback process simple - High completion rates come from easy, frictionless feedback mechanisms
Balance quantitative and qualitative data - Numbers tell you what's happening; comments tell you why
Acknowledge and reward user contributions - Let users know when their feedback has led to specific improvements

Going beyond feedback collection? We recommend reading how to implement LLM observability for production.

Turn User Feedback Into Tangible LLM Improvements ⚡️

Stop guessing what users want. Find out what's working, what's failing, and where to focus development efforts with Helicone's user response tracking and feedback tooling.

Start Collecting Feedback for Free 🔥

Useful Resources

For AI developers out there, what are some top of mind problems you're facing right now?

Lina Lam — Thu, 20 Feb 2025 18:39:55 +0000

A round up of top ai inference platforms this year!

Lina Lam — Fri, 24 Jan 2025 00:08:13 +0000

Top 10 AI Inference Platforms in 2025

Lina Lam ・ Jan 24

#api #llm #ai

Top 10 AI Inference Platforms in 2025

Lina Lam — Fri, 24 Jan 2025 00:07:19 +0000

The development of Large Language Model (LLM) applications is accelerating rapidly, driven by the need for automation, operational efficiency, and advanced insights. These breakthroughs rely on AI inferencing platforms, which enable natural language understanding and generation at scale.

Selecting the right platform is pivotal to ensuring optimal performance, scalability, and cost-effectiveness for your AI products.

In this guide, we highlight the top AI inferencing platforms in 2025, including Together AI, Fireworks AI, Hugging Face, and others to help you identify the ideal option for your needs. If you're exploring alternatives to OpenAI, this guide will help you make an informed decision.

Overview of the Top AI Inferencing Platforms

Together AI
Fireworks AI
Hyperbolic
Replicate
Hugging Face
Groq
DeepInfra
OpenRouter
Lepton
Perplexity AI
Anyscale

For developers looking for AI observability, check out Helicone.

Try it for free 🔥

1. Together AI

Best for: Large-scale model training with a focus on privacy and cost efficiency.

What is Together AI?

Together AI offers high-performance inference for 200+ open-source LLMs with sub-100ms latency, automated optimization, and horizontal scaling - all at a lower cost than proprietary solutions. Their infrastructure handles token caching, model quantization, and load balancing, letting developers focus on prompt engineering and application logic rather than managing infrastructure.

Why do companies use Together AI?

Together AI's pricing makes it up to 11x more affordable than GPT-4 when using Llama-3, 4x faster throughput than Amazon Bedrock, and 2x faster than Azure AI.

Developers can access 200+ open-source models including Llama 3, RedPajama, and Falcon with just a few lines of Python, making it straightforward to swap between models or run parallel inference jobs without managing separate deployments or wrestling with CUDA configurations.

Together AI Pricing

Free tier available; pay per token or GPU usage for serverless options.

Bottom Line

Together AI is ideal for developers who wants access to a wide range of open-source models. With flexible pricing and high-performance infrastructure, it's a strong choice for companies that require custom LLMs and a scalable solution that is optimized for AI workloads.

Integrate LLM Observability with Helicone

Create an Helicone account, then change your baseurl. See docs for details.

base_url="https://together.helicone.ai/v1"

2. Fireworks AI

Best for: Speed and scalability in multi-modal AI tasks.

What is Fireworks AI?

Fireworks AI has one of the fastest model APIs. It uses its proprietary optimized FireAttention inference engine to power text, image, and audio inferencing, all while prioritizing data privacy with HIPAA and SOC2 compliance. It also offers on-demand deployment as well as fine-tuning text models to use either serverless or on-demand.

Why do companies use Fireworks AI?

Fireworks makes it easy to integrate state-of-the-art multi-modal AI models like FireLLaVA-13B for applications that require both text and image processing capabilities. Fireworks AI has 4x lower latency than other popular open-source LLM engines like vLLM, and ensures data privacy and compliance requirements with HIPAA and SOC2 compliance.

Fireworks AI Pricing

All services are pay-as-you-go. Get started here.

Bottom Line

Fireworks is ideal for companies looking to scale their AI applications. Moreover, developers can integrate Fireworks with Helicone to get production-grade LLM infrastructure with built-in observability and real-time cost and usage monitoring.

Integrate LLM Observability with Helicone

Create an Helicone account, then change your baseurl. See docs for details.

base_url="https://fireworks.helicone.ai/inference/v1/completions"

3. Hyperbolic

Best for: Developers looking for cost-effective GPU rental and API access.

What is Hyperbolic?

Hyperbolic is a platform that provides AI inferencing service, affordable GPUs, and accessible compute for anyone who interacts with the AI system — AI researchers, developers, and startups to build AI projects at any scale.

Why do companies use Hyperbolic?

Hyperbolic provides access to top-performing models for Base, Text, Image, and Audio generation at up to 80% less than the cost of traditional providers without compromising quality. They also guarantee the most competitive GPU prices compared to large cloud providers like AWS. To close the loop in the AI ecosystem, Hyperbolic partners with data centers and individuals who have idle GPUs.

Hyperbolic Pricing

The base plan is free to start, catered to startups and small to medium-sized enterprises that need higher throughput and advanced features. Premium pricing model is geared toward academic and advanced enterprise use. Get started here.

Bottom Line

Hyperbolic's strength lies in providing both inference access and compute at a fraction of the cost. For those looking to serve state-of-the-art models at a competitive price or research-grade scaling, Hyperbolic would be a suitable option. You can easily integrate Hyperbolic with Helicone to monitor and optimize your LLM applications.

Integrate LLM Observability with Helicone

Create an Helicone account, then change your baseurl. See docs for details.

base_url="https://hyperbolic.helicone.ai/v1"

4. Replicate

Best for: Rapid prototyping and experimenting with open-source or custom models.

What is Replicate?

Replicate is a cloud-based platform that simplifies machine learning model deployment and scaling. Replicate uses an open-source tool called Cog to package and deploy models, and supports a diverse range of large language models like Llama 2, image generation models like Stable Diffusion, and many others.

Why do companies use Replicate?

Replicate is great for quick experiments and building MVPs (model performance varies based on user uploads). Replicate has thousands of pre-built, open-source models covering a wide range of applications like text generation, image processing, and music generation - and getting started requires just one line of code.

Replicate Pricing

Based on usage with a pay-per-inference model. Get started here.

Bottom Line

Replicate scales well for small to medium workloads but may need extra infrastructure for high-volume apps. It's a great choice for experimentation and for developers who need quick access to models without the setup and overhead.

5. HuggingFace

Best for: Getting started with Natural Language Processing (NLP) projects.

What is HuggingFace?

HuggingFace is an open-source community where developers can build, train, and share machine learning models and datasets. It's most popularly known for its transformer library. HuggingFace makes it easy to collaborate, and it's a great starting point for many NLP projects.

Why do companies use HuggingFace?

HuggingFace has an extensive model hub with over 100,000 pre-trained models such as BERT and GPT. It also integrates with different languages and cloud platforms, providing scalable APIs that easily extend to services like AWS.

HuggingFace Pricing

Free for basic use; enterprise plans available. Get started here.

Bottom Line

HuggingFace has a strong emphasis on open-source development, so you may find inconsistency in documentation, or have trouble finding examples for complex use cases. However, HuggingFace is a great library of pre-trained models for fine-tuning and AI inferencing — which is useful for many NLP use cases.

6. Groq

Best for: High-performance inferencing with hardware optimization.

What is Groq?

Groq specializes in hardware optimized for high-speed inference. Its Language Processing Unit (LPU), a specialized chip built for ultra-fast AI inference, significantly outperforms traditional GPUs, providing up to 18x faster processing speeds for latency-critical AI applications.

Why do companies use Groq?

Groq scales exceptionally well in performance-critical applications. In addition, Groq provides both cloud and on-premises solutions, making it a suitable option for high-performance AI applications across industries. Groq is suited for enterprises that require high-performance, on-premises solutions.

Groq Pricing

Token-based pricing, geared towards enterprise use. Get started here.

Bottom Line

If ultra-low latency and hardware-level optimization are critical for your application, using LPU can give you a significant advantage. However, you may need to adapt your existing AI workflows to leverage the LPU architecture.

Integrate LLM Observability with Helicone

Create an Helicone account, then change your baseurl. See docs for details.

base_url="https://groq.helicone.ai/openai/v1"

7. DeepInfra

Best for: Cloud-based hosting of large-scale AI models.

What is DeepInfra?

DeepInfra offers a robust platform for running large AI models on cloud infrastructure. It's easy to use for managing large datasets and models. Its cloud-centric approach is best for enterprises needing to host large models.

Why do companies use DeepInfra?

DeepInfra's inference API takes care of servers, GPUs, scaling, and monitoring, and accessing the API takes just a few lines of code. It supports most OpenAI APIs to help enterprises migrate and benefit from the cost savings. You can also run a dedicated instance of your public or private LLM on DeepInfra infrastructure.

DeepInfra Pricing

Usage-based, billed by token or at execution time. Get started here.

Bottom Line

DeepInfra is a good option for projects that need to process large volumes of requests without compromising performance.

Integrate LLM Observability with Helicone

Create an Helicone account, then change your baseurl. See docs for details.

base_url=f"https://deepinfra.helicone.ai/{HELICONE_API_KEY}/v1"

8. OpenRouter

Best for: Routing traffic across multiple LLMs.

What is OpenRouter?

OpenRouter is a unified platform designed to help users find the best LLM models and prices for their prompts. OpenRouter Runner is the monolith inference engine built with Modal that powers open-source models that are hosted in a fallback capacity on OpenRouter.

Why do companies use OpenRouter?

OpenRouter has a remarkably user-friendly interface and a broad range of model selection. It allows developers to route traffic between multiple LLM providers for optimal performance, which is ideal for developers managing multiple LLM environments.

OpenRouter Pricing

Pay-as-you-go and subscription plans. Get started here.

Bottom Line

OpenRouter is a great option for developers who want flexibility in switching between LLM providers. If you need to use different models without the hassle of integrating separate APIs, OpenRouter simplifies the process. However, you do have less control over exact model versions, which could be a limitation depending on your use case.

Integrate LLM Observability with Helicone

Create an Helicone account, then change your baseurl. See docs for details.

base_url=f""https://openrouter.helicone.ai/api/v1/chat/completions"

9. Lepton AI

Best for: Enterprises that require scalable and high-performance AI capabilities.

What is Lepton?

Lepton is a Pythonic framework to simplify AI service building. The Lepton Cloud offers AI inferencing and training with cloud-native experience and GPU infrastructure. Developers use Lepton for efficient and reliable AI model deployment, training, and serving, and high-resolution image generation and serverless storage.

Why do companies use Lepton?

The platform offers a simple API that allows developers to integrate state-of-the-art models into any application easily. Developers can create models using Python without the need to learn complex containerization or Kubernetes, then deploy them within minutes.

Lepton Pricing

Usage-based and subscription plans. The free plan currently supports up to 48 CPUs + 2 GPUs concurrently, while each serverless endpoint costs by 1 million tokens. Get started here.

Bottom Line

Lepton can be a good fit for enterprises that need fast language processing without heavy resource consumption. However, Lepton focuses on Python, which limits options for those working with other languages.

10. Perplexity AI

Best for: AI-driven search and knowledge applications.

What is Perplexity?

Perplexity AI is known for its AI-powered search and answer engine. While primarily a consumer-facing service, they offer APIs for developers to access intelligent search capabilities. pplx-api is a new service designed for fast access to various open-source language models.

Why do companies use Perplexity?

Developers can quickly integrate state-of-the-art open-source models via the familiar REST API. Perplexity is also rapidly including new open-source models like Llama and Mistral within hours of launch.

Perplexity Pricing

Usage or subscription-based. Pro users receive a recurring $5 monthly pplx-api credit. For all other users, pricing will be determined based on usage. Get started here.

Bottom Line

Perplexity AI is suitable for developers looking to incorporate advanced search and Q&A capabilities into their applications. If improving information retrieval is a crucial aspect of your project, using Perplexity can be a good move.

11. AnyScale

Best for: End-to-end AI development and deployment and applications requiring high scalability.

What is AnyScale?

AnyScale offers distributed computing, scalable model serving, and an end-to-end platform for developing, training, and deploying models. AnyScale is the company behind RayTurbo — a framework for scaling Python applications and an AI compute engine optimized for performance, efficiency, and reliability.

Why do companies use AnyScale?

AnyScale offers governance, admin, and billing controls as well as security and privacy features suitable for enterprise-grade applications. AnyScale is also compatible with any cloud, accelerator, or stack, and has expert support from Ray, AI, and ML specialists.

AnyScale Pricing

Usage-based, enterprise pricing available. Get started here.

Bottom Line

AnyScale is ideal for developers building applications that require high scalability and performance. If your project uses Python and you are at the scaling stage, Anyscale can be a good option.

Integrate LLM Observability with Helicone

Create an Helicone account, then change your baseurl. See docs for details.

Helicone-OpenAI-API-Base: https://api.endpoints.anyscale.com/v1

Choosing the Right API Provider

When choosing an AI inferencing platform, it's essential to consider your specific project requirements, whether it's affordability, speed, scalability, or advanced functionality.

Use case	Recommendation
For high performance and privacy	Together AI offers high-quality responses, faster response time, and lower cost, with a focus on privacy and scalability.
For cost-effective solutions	Hyperbolic provides access to top-performing models at a fraction of the cost, with competitive GPU prices.
For rapid prototyping and experimentation	Replicate simplifies machine learning model deployment and scaling, ideal for quick experiments and building MVPs.
For NLP projects and open-source models	HuggingFace provides an extensive library of pre-trained models and a strong open-source community.
For ultra-low latency applications	Groq specializes in hardware optimized for high-speed inference with their Language Processing Unit (LPU).
For large-scale AI applications	DeepInfra excels in hosting and managing large AI models on cloud infrastructure.
For flexibility across multiple LLM providers	OpenRouter allows routing traffic between multiple LLM providers for optimal performance.
For enterprises requiring scalable AI capabilities	Lepton AI offers a Pythonic framework for efficient and reliable AI model deployment and training.
For AI-driven search and knowledge applications	Perplexity AI specializes in AI-powered search engines and knowledge retrieval.

Remember to consider factors such as pricing, model variety, ease of integration, and scalability when making your final decision. It's often beneficial to start with a small-scale test before committing to a provider for large-scale deployment.

GPT-5: Release Date, Features & Everything You Need to Know

Lina Lam — Thu, 05 Dec 2024 18:47:56 +0000

OpenAI's GPT-5 is the next anticipated breakthrough in OpenAI's language model series. Although its release is slated for early 2025, this guide covers everything we know so far, from projected capabilities to potential applications.

When is GPT-5 coming out?

According to recent statements, GPT-5 is expected to be released in early 2025. In the meantime, OpenAI will be focusing on GPT-o1, previously codenamed "Project Strawberry". This model takes a more methodological and slower approach to support tasks in mathematics, science, and other areas requiring accuracy and logical reasoning. OpenAI faces limitations in shipping multiple models in parallel.

All of these models have gotten quite complex and we can't ship as many things in parallel as we'd like to. We also face a lot of limitations and hard decisions about [where] we allocate...our computers towards.

What can we expect from GPT-5?

There hasn’t been specific benchmarks released of GPT-5 compared to past models. However, GPT-5 is expected to introduce significant advancement based on the trends observed in previous GPT iterations. Here's what you can expect:

1. Better Performance

GPT-5 is likely to surpass GPT-4o and GPT-o1 in complex reasoning tasks. It may achieve higher accuracy in STEM fields, potentially exceeding GPT-o1's 83% on International Mathematics Olympiad (IMO) qualifying exams. Moreover, GPT-5 might introduce architectural innovations that improve its efficiency, potentially allowing it to run on smaller devices or with lower computational resources.

2. Multimodal Capabilities

GPT-5 is expected to offer more seamless integration of text, images, audio, and video processing, improving upon GPT-4o's multimodal capabilities. Unlike GPT-4o, which handles text, images, and voice, GPT-5 is anticipated to work with audiovisual data in a more cohesive manner.

3. Larger Context Window

We are anticipating significant increase in context window size, potentially allowing for processing of much longer inputs and outputs.

4. Better Reasoning Abilities

GPT-5 may build upon GPT-o1's Chain-of-Thought reasoning, offering even more sophisticated problem-solving capabilities.

5. Increased Task Complexity

GPT-5 is speculated to handle more complex tasks, possibly up to "five-hour tasks" with up to 1,000 discrete steps.

It's important to note that these are speculative improvements based on industry trends and statements from OpenAI executives. The actual capabilities of GPT-5 will only be known upon its release, which is anticipated in early 2025.

What is the difference between GPT-4 and GPT-5?

GPT-5 is expected to introduce several advancements including multilingual support over GPT-4. The new model will incorporate more advanced architectures like graph neural networks and enhanced attention mechanisms, enabling more efficient and accurate language processing.

GPT-5 will also leverage unsupervised learning on a larger and more diverse dataset, allowing it to better understand complex language, including concepts like sarcasm and irony. Additionally, GPT-5 is anticipated to support multiple languages and have an even greater number of parameters, potentially over 200 billion, further enhancing its text generation and multimodal capabilities compared to GPT-4.

How much better is GPT-5?

GPT-5's accuracy and precision are expected to be higher than GPT-4o, though the exact figures are not yet available. For reference, GPT-4o has an accuracy rate of 89% in understanding and responding to contextually complex queries, compared to 84% from its predecessor GPT-4. For precision, GPT-4o achieves 87% in generating relevant responses, outperforming GPT-4 (82%), GPT-3.5 (78%) and GPT-3 (73%).

While specific numbers for GPT-5 are not provided, Sam Altman, CEO of OpenAI, has expressed that GPT-5 is anticipated to be "a lot smarter than GPT-4" in a podcast with Lex Fridman, further explaining that GPT-5 is expected to have improved reasoning abilities, higher accuracy rates, and faster processing speeds compared to its predecessors. Additionally, GPT-5 aims to consistently provide the best response out of 10,000 potential answers, significantly improving reliability over GPT-4.

Save up to 70% on your API cost ⚡️

Helicone users can cache their responses, optimize prompts and more.

What training data does GPT-5 use?

GPT-5's training data is expected to be extensive and diverse, combining approximately 70 trillion tokens across 281 terabytes of data, including publicly available data and purchased datasets. The data also includes around 50 trillion tokens of synthetic data to enhance the model's capabilities.

FAQ

What is GPT-o1, and how is it different from previous models?

GPT-o1, formerly known as Project Strawberry, is a new model designed to excel at tasks requiring advanced logical reasoning, accuracy, and step-by-step problem-solving.

Unlike earlier models such as GPT-4o, GPT-o1 integrates Chain-of-Thought reasoning and AI Reinforcement Learning to handle complex STEM-related problems more effectively. In fact, GPT-o1 outperforms GPT-4o, achieving 83% accuracy on IMO qualifying exams and performing at a PhD-level in STEM tasks. In addition to improved accuracy and reasoning capabilities, GPT-o1 is safer, better at mitigating biases, and can produce longer outputs of up to 26 pages.

However, these benefits come with certain trade-offs: GPT-o1 has slower response times and higher costs, making it a more specialized tool for complex reasoning scenarios. Conversely, GPT-4o remains better suited for general-purpose applications where speed and efficiency are paramount.

Why is OpenAI delaying the release of GPT-5?

The complexity and scale of OpenAI’s models have grown significantly, making it challenging to develop multiple advanced systems in parallel. By focusing on GPT-o1 this year, OpenAI aims to better allocate its computing resources and ensure higher quality, more reliable performance before moving on to the next major version.

How does OpenAI’s approach compare to competitors like Meta and Google?

OpenAI has adopted an aggressive and forward-looking approach, continually launching new products and upgrading existing models to stay ahead of competitors like Meta and Google. While all companies work on advancing AI capabilities, OpenAI’s current focus is on refining performance and reliability rather than simply pushing rapid major releases.

What are some of the anticipated use cases and applications for GPT-5?

GPT-5 is expected to excel at complex reasoning tasks, demonstrate stronger comprehension and multilingual support, and have enhanced multimodal integration compared to prior GPT models. This could enable more advanced applications in areas like scientific research, data analysis, and conversational AI.

Questions or feedback?

Are the information out of date? Please raise an issue and we’d love to hear your insights!

GPT-5: Release Date, Features & Everything You Need to Know

Lina Lam — Thu, 05 Dec 2024 18:47:56 +0000

When is GPT-5 coming out?

All of these models have gotten quite complex and we can't ship as many things in parallel as we'd like to. We also face a lot of limitations and hard decisions about [where] we allocate...our computers towards.

What can we expect from GPT-5?

1. Better Performance

2. Multimodal Capabilities

3. Larger Context Window

We are anticipating significant increase in context window size, potentially allowing for processing of much longer inputs and outputs.

4. Better Reasoning Abilities

GPT-5 may build upon GPT-o1's Chain-of-Thought reasoning, offering even more sophisticated problem-solving capabilities.

5. Increased Task Complexity

GPT-5 is speculated to handle more complex tasks, possibly up to "five-hour tasks" with up to 1,000 discrete steps.

What is the difference between GPT-4 and GPT-5?

How much better is GPT-5?

Save up to 70% on your API cost ⚡️

Helicone users can cache their responses, optimize prompts and more.

What training data does GPT-5 use?

FAQ

What is GPT-o1, and how is it different from previous models?

GPT-o1, formerly known as Project Strawberry, is a new model designed to excel at tasks requiring advanced logical reasoning, accuracy, and step-by-step problem-solving.

Why is OpenAI delaying the release of GPT-5?

How does OpenAI’s approach compare to competitors like Meta and Google?

What are some of the anticipated use cases and applications for GPT-5?

Questions or feedback?

Are the information out of date? Please raise an issue and we’d love to hear your insights!

Prompt engineering AI-Spreadsheet-like experience 🚀

Lina Lam — Thu, 03 Oct 2024 19:33:55 +0000

Hello everyone! 👋 This is the Helicone team, we're beyond excited to announce Helicone Experiments - a new way to perfect your prompts. 🚀

Crafting the perfect prompt is extremely difficult. Testing, tweaking, and iterating, the process is tedious and time consuming. But there is a better way.

Today, we are redefining prompt engineering to help you 10x your workflow.

5 Powerful Techniques to Slash Your LLM Costs

Lina Lam — Wed, 04 Sep 2024 16:53:15 +0000

Building AI apps isn’t as easy (or cheap) as you think
Building an AI app might seem straightforward — with the promise of powerful models like GPT-4 at your disposal, you’re ready to take the world by storm.

But as many developers and startups quickly discover, the reality isn’t so simple. While creating an AI app isn’t necessarily hard, costs can quickly add up, especially with models like GPT-4 Turbo charging 1 to 3 cents per 1,000 input/output tokens.

The hidden cost of AI workflows

Sure, you could opt for cheaper models like GPT-3.5 or an open-source alternative like Llama, throw everything into one API call with excellent prompt engineering, and hope for the best. However, this approach often falls short in production environments.

AI’s current state means that even a 99% accuracy rate isn’t enough; that 1% failure can break a user’s experience. Imagine a major software company operating at this level of reliability—it’s simply unacceptable.

Whether you’re wrestling with bloated API bills or struggling to balance performance with affordability—there are effective strategies to tackle these challenges. Here’s how you can keep your AI app costs in check without sacrificing performance.

We published the 5 top tips to slash your LLM cost:

Optimize your prompts
Implement response caching
Use task-specific, smaller models
Use RAG instead of sending everything to the LLM
Use LLM observability tools.

Visit the full post here.

How to Automate Your Product Hunt Launch: Lessons from Helicone's Success

Lina Lam — Wed, 28 Aug 2024 23:09:52 +0000

Hello dev community! 👋 I came across this great article by Helicone AI about automating Product Hunt launches. They recently just received #1 Product of the Day, and thought I'd share the key takeaways:

Why automate?

Reach a wider audience efficiently
Maintain consistent engagement across time zones
Free up time for real-time interaction
Drive targeted actions through automated messaging
Reduce stress during launch

4 key automation strategies:

Automate early morning user emails
- Prepare content in advance
- Use email marketing tools to schedule
- Segment audience by time zones
Schedule social media content
- Create a content calendar
- Use scheduling tools like Typefully
- Mix text, images, and videos
- High-performing content: memes, founder updates, challenges, behind-the-scenes
Implement a drip DM campaign
- Build your LinkedIn network in advance
- Use LinkedIn Premium for better capabilities
- Create a simple, clear DM template
- Consider automation tools (but be aware of ToS)
- Time campaigns strategically across time zones
The final 10%: manual efforts
- Create day-of content
- Engage in comments
- Leverage personal networks
- Go the extra mile (e.g., office cookies, virtual launch party)

Pro tips:

Don't share direct links to your product page
DMing individuals is more effective than general posts
Most leads came from LinkedIn, not Product Hunt
Consider working with an experienced Product Hunt launcher

Has anyone here launched on Product Hunt? What was your experience like? Any additional tips to share?

For the full article, read here.

What is LLM Observability and Monitoring?

Lina Lam — Wed, 31 Jul 2024 16:54:50 +0000

Building with LLMs in production (well) is incredibly difficult. You probably have heard of the word LLM Observability. But what is it? How does it differ from traditional observability? What is being observed? Our team at Helicone AI have the answers.

The TL;DR

LLM Observability is complete visibility into every layer of an LLM-based software system - the application, the prompt, and the response. LLM Observability comes hand-in-hand with LLM Monitoring. While monitoring tracks application performance metrics, observability is more investigative.

	LLM Observability	LLM Monitoring
Purpose	Event logging	Collect metrics
Key Aspects	Trace the flow of requests to understand system dependencies and interactions	Track application performance metrics, such as usage, cost, latency, error rates
Example	Correlate different types of data to understand issues and complex behaviours	Set up thresholds for unexpected behaviors

What's the difference between LLM vs. Traditional Observability?

Traditional development is typically transactional. Developers observe how the application handles HTTP requests/responses, a database query, or published message. In contrast, LLMs are much more complex.

Here's a comparison of the logs:

Traditional	LLMs
Simple, isolated interactions	Indefinitely nested interactions, creating a complex tree structure
Clear start and end points	Encompass multiple interactions
Small body size (low KBs of data)	Massive payloads (potentially GBs)
Predictable behavior (easy to evaluate)	Lack of predictability (difficult to evaluate)
Primarily text-based logs and numerical metrics	Multi-modal data (text, image, audio, video)

Issues with LLMs

Hallucination: LLMs' objective is to predict the next few characters and not accuracy. This means that responses are not grounded in facts.

Complex use cases: LLM-based software systems require an increasing number of LLM calls to execute a complex task (i.e. agentic workflow). Reflexion is a technique engineers use to get LLMs to analyze their own results. But this consists of having multiple calls inside of multiple spans for checking hallucinations.

Proprietary data: Managing proprietary data is tricky. You need it to answer specific customer questions, but it can accidentally find its way into the responses.

Quality of response: Is the response in the wrong tone? Is the amount of detail appropriate for your users' ask?

Cost (the big elephant in the room) - As usage goes up, and your LLM setup becomes more complicated (i.e. adding Reflexion), the cost can easily add up.

Third-party models: Their API can change, new models and new guardrails can be added, causing your LLM app to behave differently than before.

Limited competitive advantage: LLMs are hard to train and maintain. Chances are that you are using the same model as your competitor. Your differentiator becomes your prompt engineering and proprietary data.

What LLM Observability Tools Have In Common

Developers working on LLM applications need effective tools to understand and address bugs, and exceptions, and prevent regressions. They require unique visibility into the functioning of these applications, including:

Real-time monitoring of AI models
Detailed error tracking and reporting
Insights into user interactions and feedback
Performance metrics and trend analysis
Multi-metric correlations
Tools for prompt iterations and experimentation

The author

Aparna Dhinakaran is the Co-Founder and Chief Product Officer at Arize AI, a leader in machine learning observability. She is recognized in Forbes 30 Under 30 and led ML engineering at Uber, Apple, and TubeMogul (Adobe).

What we've learned

At Helicone AI, we've seen the complexities of productizing LLMs first-hand. Effective observability is key to navigating these challenges, and we strive to help our customers produce reliable and high-quality LLM applications, making the observability process easier and faster.

What are your thoughts?

What is LLM Observability and Monitoring?

Lina Lam — Wed, 31 Jul 2024 16:54:50 +0000

Building with LLMs in production (well) is incredibly difficult. You probably have heard of the word LLM Observability. But what is it? How does it differ from traditional observability? What is being observed? We have the answers.

The TL;DR

	LLM Observability	LLM Monitoring
Purpose	Event logging	Collect metrics
Key Aspects	Trace the flow of requests to understand system dependencies and interactions	Track application performance metrics, such as usage, cost, latency, error rates
Example	Correlate different types of data to understand issues and complex behaviours	Set up thresholds for unexpected behaviors

What's the difference between LLM vs. Traditional Observability?

Here's a comparison of the logs:

Traditional	LLMs
Simple, isolated interactions	Indefinitely nested interactions, creating a complex tree structure
Clear start and end points	Encompass multiple interactions
Small body size (low KBs of data)	Massive payloads (potentially GBs)
Predictable behavior (easy to evaluate)	Lack of predictability (difficult to evaluate)
Primarily text-based logs and numerical metrics	Multi-modal data (text, image, audio, video)

Issues with LLMs

Hallucination: LLMs' objective is to predict the next few characters and not accuracy. This means that responses are not grounded in facts.

Proprietary data: Managing proprietary data is tricky. You need it to answer specific customer questions, but it can accidentally find its way into the responses.

Quality of response: Is the response in the wrong tone? Is the amount of detail appropriate for your users' ask?

Cost (the big elephant in the room) - As usage goes up, and your LLM setup becomes more complicated (i.e. adding Reflexion), the cost can easily add up.

Third-party models: Their API can change, new models and new guardrails can be added, causing your LLM app to behave differently than before.

What LLM Observability Tools Have In Common

Real-time monitoring of AI models
Detailed error tracking and reporting
Insights into user interactions and feedback
Performance metrics and trend analysis
Multi-metric correlations
Tools for prompt iterations and experimentation

The author

What we've learned

What are your thoughts?

DEV Community: Lina Lam

The Complete Guide to LLM Observability Platforms in 2025

Table Of Contents

Introduction to LLM Observability Platforms

Key Evaluation Criteria for LLM Observability Tools

1. Implementation & Time-to-Value

2. Feature Completeness

3. Technical Considerations

4. Business Factors

Types of LLM Observability Solutions

Comparing Top LLM Observability Tools

At a Glance

💡 What makes Helicone different?

Detailed Feature Comparison

Helicone: The Developer-First LLM Observability Platform

Key Differentiators

"Probably the most impactful one-line change I've seen applied to our codebase."

Architectural Advantage

Comparing Helicone vs. Alternatives

1. Helicone vs. LangSmith

💡 Bottom Line

2. Helicone vs. Langfuse

3. Helicone vs. Braintrust

4. Helicone vs. Arize Phoenix

5. Helicone vs. HoneyHive

6. Helicone vs. Traceloop (OpenLLMetry)

7. Helicone vs. Galileo

8. Helicone vs. Weights & Biases

9. Helicone vs. Portkey

10. Helicone vs. Comet

11. Building Your Own Observability Solution

See the Helicone difference for yourself

How to Choose: Decision Framework

💡 Implementation Tip

Conclusion

How to Track LLM User Feedback to Improve Your AI Applications

Table of Contents

Why is Tracking User Feedback Critical for LLM Applications?

The Feedback Collection Framework

Method 1: Implementing the Feedback API

Method 2: Using Custom Properties

Method 3: Advanced User Metrics Tracking

Pro Tip 💡

Turning User Feedback into Training Datasets

Step 1: Filtering and Exporting Your Feedback Data

Step 2: Identifying Actionable Insights

Step 3: Creating Specialized Training Datasets

Success Stories

Journalist AI: Subscription-Based Feedback Segmentation

Greptile: Repository-Specific Performance Tracking

Implementation Best Practices

Turn User Feedback Into Tangible LLM Improvements ⚡️

Useful Resources

For AI developers out there, what are some top of mind problems you're facing right now?

A round up of top ai inference platforms this year!

Top 10 AI Inference Platforms in 2025

Lina Lam ・ Jan 24

Top 10 AI Inference Platforms in 2025

Overview of the Top AI Inferencing Platforms

1. Together AI

What is Together AI?

Why do companies use Together AI?

Together AI Pricing

Bottom Line

Integrate LLM Observability with Helicone

2. Fireworks AI

What is Fireworks AI?

Why do companies use Fireworks AI?

Fireworks AI Pricing

Bottom Line

Integrate LLM Observability with Helicone

3. Hyperbolic

What is Hyperbolic?

Why do companies use Hyperbolic?

Hyperbolic Pricing

Bottom Line

Integrate LLM Observability with Helicone

4. Replicate

What is Replicate?

Why do companies use Replicate?