Helicone entered maintenance mode after its March 2026 acquisition by Mintlify. Here are the best alternatives for teams that need an actively developed AI gateway with deeper enterprise governance, MCP support, and production-grade reliability.
Why Teams Are Leaving Helicone
Helicone built a loyal following as one of the earliest LLM observability platforms, earning over 16,000 organizations as users since its Y Combinator W23 launch. Its proxy-first approach was elegant: change your OpenAI base URL, add a Helicone API key as a header, and every LLM request was automatically logged, metered, and displayed on a dashboard. No SDK installation, no instrumentation refactoring.
That chapter closed in March 2026 when Mintlify acquired Helicone. The founding team joined Mintlify in San Francisco to build AI knowledge infrastructure, and Helicone entered maintenance mode. Security patches, bug fixes, and new model support continue shipping, but active feature development has stopped. There is no roadmap for new capabilities.
For teams running production AI workloads, a maintenance-mode proxy is a risk signal. The LLM ecosystem moves fast. Provider API changes, new model architectures, evolving security threats, and emerging standards like the Model Context Protocol all demand active development from the infrastructure layer. When your gateway stops evolving, your AI stack starts accumulating technical debt.
Beyond the acquisition, Helicone had structural limitations that enterprise teams were already bumping against: no native MCP support for agentic workflows, no budget enforcement to prevent runaway spending, no VPC or air-gapped deployment options for regulated industries, and limited guardrail capabilities beyond basic logging. The platform also lacked semantic caching, relying on exact-match caching that missed semantically equivalent queries with different wording. Enterprise identity provider integration through SSO was limited to higher pricing tiers, and team-level cost attribution required custom property tagging rather than native organizational structures. The acquisition accelerated migration timelines, but the underlying gaps were already driving evaluation.
Here are the five strongest alternatives, starting with the platform that addresses Helicone's gaps most comprehensively.
1. TrueFoundry AI Gateway
Best for: Enterprise teams that need observability, governance, and cost enforcement in a single production-grade platform
TrueFoundry is the most comprehensive alternative to Helicone because it addresses every limitation that drove teams to evaluate alternatives, while adding capabilities that Helicone never offered.
Where Helicone provided observability through a proxy, TrueFoundry provides observability through an AI Gateway that also handles routing, guardrails, cost enforcement, and MCP governance. This distinction matters because observability without control is just a dashboard. TrueFoundry lets you see a cost anomaly and enforce a budget limit in the same platform, detect a prompt injection and block it through a guardrail, or identify a latency spike and reroute traffic to a faster model, all without switching tools.
TrueFoundry exposes an OpenAI-compatible API, so applications that were pointing at Helicone's proxy can redirect to TrueFoundry's gateway with a base URL change. Request logging captures prompts, completions, token counts, latency, and cost with the same granularity that Helicone users expect, plus deeper agent-level tracing that covers MCP tool calls, multi-step workflows, and retrieval chains.
Cost management goes far beyond what Helicone offered. TrueFoundry provides per-request cost tracking with attribution to teams, projects, and custom metadata tags, but it also enforces budget limits, rate limits, and automated routing to cheaper models when budgets are exhausted. Semantic and exact-match caching reduces token consumption for repetitive query patterns. These are enforcement mechanisms, not just dashboards.
Enterprise governance features include RBAC, SSO integration with providers like Okta and Azure Entra ID, audit logging for compliance frameworks like SOC 2, HIPAA, and GDPR, and fine-grained access controls through OPA and Cedar policy engines. The platform deploys within your VPC, on-premise, or in air-gapped environments, addressing the data sovereignty requirements that Helicone's cloud-hosted model could not satisfy.
The MCP Gateway provides centralized management of MCP servers with OAuth 2.0 authentication, tool-level access controls, and guardrails on tool calls, positioning TrueFoundry for the agentic AI workflows that are becoming the dominant architecture in 2026.
Performance is production-grade: approximately 3-4ms latency overhead, over 350 requests per second on a single vCPU, with horizontal scaling for higher throughput. The globally distributed SaaS gateway option is deployed across multiple regions for teams that prefer a managed service without self-hosting.
Observability extends beyond what Helicone offered. Full request traces capture not just prompts and completions but the entire agent execution path, including MCP tool invocations, retrieval steps, multi-turn conversation context, and latency breakdowns at each stage. Integration with Prometheus and Grafana means teams already running standard DevOps observability stacks can ingest TrueFoundry metrics natively. Analytics dashboards provide cost breakdowns by model, provider, team, and time period, with budget limit status and spend projections that export to standard formats for corporate finance systems.
Start migrating from Helicone →
2. Langfuse
Best for: Open-source teams that want self-hosted LLM tracing and prompt management without a gateway layer
Langfuse is the natural alternative for teams that valued Helicone primarily for its observability features rather than its gateway capabilities. With over 21,000 GitHub stars and an MIT-licensed core, Langfuse provides end-to-end tracing, prompt management, evaluation, and dataset curation. The recent acquisition by ClickHouse signals long-term investment in the platform's data infrastructure.
The migration from Helicone to Langfuse requires more integration work than a simple URL swap because Langfuse uses SDK-based instrumentation rather than a proxy model. Native SDKs for Python and TypeScript, plus connectors for LangChain, LlamaIndex, and over 50 other frameworks, cover most integration scenarios. Self-hosting is well-documented for teams with data residency requirements.
The limitation is scope. Langfuse is an observability and prompt management platform, not a gateway. It does not handle request routing, load balancing, caching, guardrails, or cost enforcement. Teams replacing Helicone with Langfuse still need a separate solution for the gateway layer. The evaluation and dataset curation features, however, are significantly deeper than what Helicone offered, making Langfuse a strong upgrade for teams whose primary concern is understanding and improving LLM output quality rather than managing infrastructure.
3. Arize AI (Phoenix)
Best for: Data science teams that need deep ML observability with RAG-specific debugging
Arize Phoenix extends ML observability to LLM applications with particular strength in embedding analysis and retrieval diagnostics. For teams running RAG pipelines, Phoenix provides debugging capabilities for retrieval quality that neither Helicone nor most other alternatives offer. The open-source core, licensed under ELv2, supports tracing, evaluation, and experimentation with OpenTelemetry compatibility.
Phoenix is a stronger fit for teams whose observability needs center on model quality and retrieval performance rather than infrastructure-level concerns like routing and cost management. The embedding-level analysis tools allow teams to visualize how retrieval quality affects output quality, identify clustering patterns in queries, and detect drift in embedding distributions over time. It complements a gateway solution rather than replacing one.
4. Datadog LLM Monitoring
Best for: Organizations already running Datadog that want LLM observability within their existing APM stack
Datadog's LLM monitoring adds AI-specific metrics to the platform's established APM and infrastructure monitoring suite. Token usage, cost-per-request, and spending trends appear alongside traditional infrastructure metrics. The integration means AI cost anomalies trigger alerts through the same channels as server health issues.
The appeal is consolidation: no new vendor, no new dashboard, no new login. The limitation is depth. Datadog treats LLM monitoring as an extension of application performance monitoring rather than a purpose-built AI observability platform. It lacks the evaluation workflows, prompt management, agent tracing, and gateway-level controls that dedicated platforms provide. For teams whose primary requirement is adding AI visibility to existing Datadog infrastructure, it is the lowest-friction option.
5. OpenObserve
Best for: Teams that want unified LLM and infrastructure observability in a single open-source deployment
OpenObserve unifies LLM observability with traditional infrastructure monitoring, covering logs, metrics, traces, and frontend monitoring in one platform. The open-source, self-hostable architecture appeals to teams with strict data control requirements, and the platform accepts telemetry from any OpenTelemetry-compatible source.
OpenObserve is strongest for platform engineering teams that want to consolidate their observability stack. The claim of 140x lower storage costs compared to alternatives matters for organizations with high data volumes and long retention requirements. LLM-specific features like evaluation, prompt management, and agent tracing are less mature than in purpose-built platforms, but active development is closing those gaps. Like Langfuse, it is an observability tool rather than a gateway, so it addresses the monitoring side of what Helicone offered but not the routing, caching, or rate limiting. Teams adopting OpenObserve typically pair it with a gateway solution like TrueFoundry for a complete replacement of Helicone's functionality.
Making the Migration Decision
The right Helicone alternative depends on what you were actually using Helicone for and what your production requirements demand.
If you used Helicone primarily as a lightweight proxy for logging and cost visibility, Langfuse or OpenObserve can replace the observability layer, though you will need a separate gateway for routing and caching. Both are open-source and self-hostable, which gives you the data control that Helicone's cloud-hosted model sometimes made difficult.
If you need the proxy functionality plus enterprise governance, TrueFoundry provides the most complete replacement. It covers everything Helicone did, observability, caching, rate limiting, and cost tracking, while adding the budget enforcement, guardrails, MCP support, and VPC deployment options that Helicone lacked. The migration is also the simplest: because TrueFoundry exposes an OpenAI-compatible API, applications previously pointing at Helicone's proxy can redirect with a base URL change.
If you are already invested in a broader monitoring stack, extending Datadog or pairing Arize with your existing infrastructure may be the path of least resistance. These approaches avoid introducing a new platform into your toolchain at the cost of less depth in AI-specific governance.
For teams currently evaluating their options, the timing is favorable. The Helicone team has committed to helping customers migrate while the platform remains in maintenance mode. Waiting until maintenance mode ends or until a critical provider API change breaks compatibility creates unnecessary risk. The question is not whether to move, but where, and the answer depends on whether you need an observability tool, a gateway, or both.
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