Helicone Alternatives in 2026: 6 Gateway and LLM Observability Tools

You are probably here because Helicone has been the gateway and you noticed the Mintlify acquisition note from March 3, 2026 that put the product in maintenance mode. The honest read is that Helicone still works for what it shipped. The harder question is whether you should keep it for live traffic, add a second platform for the gaps, or migrate to one tool that does gateway, observability, evals, simulation, and guardrails together. This guide compares the six alternatives that actually move teams off Helicone in 2026.

TL;DR: Best Helicone alternative per use case

If you only read one row: pick FutureAGI when you need gateway and observability in the same OSS stack, Portkey when the requirement is a hosted enterprise gateway, and LiteLLM when the simplest path is a Python proxy that does not pull in a UI. For deeper reads: see our LLM Gateways guide, the Agent Command Center page, and the traceAI tracing layer.

Who Helicone is and where it stops

Helicone is an Apache 2.0 LLM observability platform with an OpenAI-compatible AI Gateway. The strongest use case is reducing time-to-first-trace by changing the base URL of an existing OpenAI client. Once requests flow through Helicone, the dashboard surfaces logs, sessions, user metrics, cost tracking, p95 and p99 latency, model usage, alerts, reports, HQL, eval scores, datasets, prompts, and prompt assembly. The repo on GitHub shows continued maintenance work since the acquisition note.

Helicone pricing is straightforward. Hobby is free with 10,000 requests, 1 GB storage, 1 seat, and 1 organization. Pro is $79 per month with unlimited seats, alerts, reports, and HQL. Team is $799 per month with 5 organizations, SOC 2, HIPAA, and a dedicated Slack channel. Enterprise is custom and adds SAML SSO, on-prem deployment, and bulk cloud discounts. Above included allowances, requests, storage, and seats meter at usage rates documented on the pricing page.

The acquisition note matters. On March 3, 2026, Helicone said it had joined Mintlify and that services would remain live in maintenance mode with security updates, new models, bug fixes, and performance fixes. That is a real status. It does not break anything that exists today. It does change the buying calculation for a team that wants gateway features still being shipped in 2027. The right reading is: Helicone is fine if your gateway needs match what is in the product now, and worth re-evaluating if your roadmap depends on net-new gateway features.

The other gap is product scope. Helicone has eval scores, datasets, and feedback, but it is not a deep eval platform. There is no simulated-user product. There is no integrated guardrail layer with prompt-injection blocking, PII redaction, jailbreak detection, and tool-call enforcement under one policy engine. There is no prompt optimization loop that takes failing traces and ships a versioned prompt back through CI gates. Each of those is a real reason to compare alternatives even before the Mintlify note.

The 6 Helicone alternatives compared

1. FutureAGI: Best for unified gateway + eval + observe + simulate + optimize + guard

Open source. Self-hostable. Hosted cloud option.

Most tools in this list pick one job. Helicone does request analytics. Portkey does hosted gateway. LiteLLM does provider unification. Langfuse does observability. OpenRouter does credit billing. LangSmith does LangChain ergonomics. FutureAGI does the loop, with the gateway built in. The Agent Command Center gateway and traceAI tracing layer share the same span tree, the same eval contract, the same prompt registry, and the same guardrail policy engine. A request that flows through the gateway carries its trace, scores, and policy decisions back into the same product surface where evals run.

Architecture: gateway and observability share a span tree. The Go-based Agent Command Center gateway accepts OpenAI-compatible HTTP, routes across 100+ providers, applies cache policy and rate limits, enforces PII redaction and prompt-injection guardrails, and emits OTel spans into the same ClickHouse-backed trace store traceAI writes to. Eval scores attach as span attributes, so a failing Groundedness check in production lands as a row in the same dashboard a pre-prod simulation run does. The repo is Apache 2.0 and self-hostable. Plumbing under it (Django, React/Vite, Postgres, ClickHouse, Redis, object storage, workers, Temporal, OTel across Python, TypeScript, Java, and C#) exists so the gateway and the eval loop do not need glue code.

Pricing: FutureAGI starts at $0/month. The free tier includes 50 GB tracing and storage, 2,000 AI credits, 100,000 gateway requests, 100,000 cache hits, 1 million text simulation tokens, 60 voice simulation minutes, unlimited datasets, unlimited prompts, unlimited dashboards, 3 annotation queues, 3 monitors, unlimited team members, and unlimited projects. Usage after the free tier starts at $2/GB storage, $10 per 1,000 AI credits, $5 per 100,000 gateway requests, $1 per 100,000 cache hits, $2 per 1 million text simulation tokens, and $0.08 per voice minute. Boost is $250 per month, Scale is $750 per month, and Enterprise starts at $2,000 per month.

Best for: Pick FutureAGI when the gateway and the observability layer should share the same trace tree, the same eval contract, and the same policy engine. The buying signal is teams running Helicone for routing and analytics plus a separate eval platform plus a separate guardrail tool, watching the three drift apart in production. RAG agents, voice agents, support automation, and BYOK LLM-as-judge teams fit this shape.

Skip if: Skip FutureAGI if your immediate need is a faster gateway swap on existing OpenAI clients with no eval product wanted. Helicone or LiteLLM is closer to that shape. FutureAGI also has more moving parts to self-host, especially ClickHouse, Temporal, queues, and OTel ingestion. Use the hosted product if you do not want to operate that surface.

2. Portkey: Best for hosted enterprise gateway

Hosted gateway. Gateway repo MIT. Self-host on Enterprise for full control plane.

Portkey is the strongest hosted alternative when the requirement is a polished gateway with budgets, fallbacks, semantic caching, prompt management, observability, and enterprise governance. The SDK is open source, the Portkey Gateway repo is MIT, and the production product is hosted with self-host available for the OSS gateway and full hosted control-plane on the Enterprise plan.

Architecture: Portkey covers an AI gateway with provider routing, fallbacks, retries, semantic and simple caching, rate limits, budgets, virtual keys, prompt management, and observability with logs, metrics, and dashboards. The docs describe Python and TypeScript SDKs, OpenAI-compatible endpoints, and integrations with Anthropic, Google Vertex, AWS Bedrock, Azure OpenAI, Mistral, Cohere, Groq, Together, and others. The Portkey Gateway repo is MIT.

Pricing: Portkey has a free tier with 10,000 recorded logs per month and basic features. Production starts at $49 per month with 100,000 recorded logs and overages around $9 per additional 100,000. Enterprise is custom and includes private cloud, SAML SSO, SOC 2, HIPAA, on-prem deployment, and dedicated support. Verify the current pricing page since limits and feature gates change.

Best for: Pick Portkey if your team wants a hosted gateway with strong governance, budget controls, and prompt management, and your eval workflow lives in another tool. It pairs well with Langfuse or Braintrust for evals and a separate tracing layer.

Skip if: Skip Portkey if you want one platform for gateway and deep evals plus simulation plus guardrails plus optimization. Portkey covers the gateway and observability surface well, but eval depth, simulation, and prompt optimization are not the center of gravity. Also model the cost if your trace volume is high since hosted log retention adds up.

3. LiteLLM: Best for a Python proxy at the SDK boundary

Open source. Self-hostable. LiteLLM Cloud option.

LiteLLM is the right alternative when the simplest path to value is a Python proxy or library that exposes 100+ LLM providers behind an OpenAI-compatible API. It is the lightest-weight gateway option, runs as a single service, and gets you out of provider lock-in without buying a UI.

Architecture: LiteLLM is MIT and supports Anthropic, AWS Bedrock, Google Vertex, Azure OpenAI, Cohere, Mistral, Together, Groq, OpenRouter, Replicate, HuggingFace, and many more. The proxy ships logging callbacks for Langfuse, Braintrust, OpenTelemetry, and others. Auth, rate limits, virtual keys, budgets, and team management are part of the proxy product, with a UI in the LiteLLM Admin app.

Pricing: LiteLLM is open source and free. LiteLLM Cloud is usage-based hosted on top of the same proxy. Verify current cloud pricing since it has changed across 2024-2026. The community edition is free under MIT.

Best for: Pick LiteLLM if you want a single Python service in front of all your providers, do not need an opinionated UI, and prefer to wire callbacks into your existing eval and observability tools. It is the standard pick for teams that already use OpenAI client SDKs and want to swap providers without rewriting application code.

Skip if: Skip LiteLLM if you want a polished UI for analytics, prompts, evals, and guardrails out of the box. It is a proxy first. The dashboard exists, but it is not the focus, and most teams pair it with Langfuse, Braintrust, or FutureAGI for the deeper observability and eval surface. Also note that LiteLLM had a security incident in 2025 which is worth reading before deploying as the front door of your traffic.

4. Langfuse: Best for OSS-first tracing without a built-in gateway

Open source core. Self-hostable. Hosted cloud option.

Langfuse is the right alternative when the main requirement is observability, prompt management, datasets, and evals, and the gateway is owned by another tool. Pair Langfuse with LiteLLM, Portkey, OpenRouter, or a direct provider SDK for routing, and use Langfuse as the trace and eval system of record.

Architecture: Langfuse covers tracing, prompt management, evaluation, datasets, playgrounds, human annotation, public APIs, and OTel ingestion. The self-hosting docs require Postgres, ClickHouse, Redis or Valkey, object storage, workers, and application services. Most of the repo is MIT, with enterprise directories handled separately.

Pricing: Langfuse Cloud Hobby is free with 50,000 units, 30 days data access, 2 users, and community support. Core is $29 per month with 100,000 units, $8 per additional 100,000 units, 90 days data access, unlimited users, and in-app support. Pro is $199 per month with 3 years data access, retention management, unlimited annotation queues, SOC 2, and ISO 27001 reports. Enterprise is $2,499 per month.

Best for: Pick Langfuse if you need self-hosted tracing, prompt versioning, datasets, eval scores, annotation queues, and OTel compatibility, and your gateway is already chosen. It pairs well with LiteLLM proxies, custom scorers, and existing CI eval jobs.

Skip if: Skip Langfuse if you need a built-in gateway, simulation, voice scoring, or prompt optimization in the same product. Those workflows are stitched in. Also be precise on OSS in procurement: most code is MIT, but enterprise directories are separate.

5. OpenRouter: Best for hosted unified API across many providers

Closed platform with OSS clients. Hosted only.

OpenRouter is the right alternative when the requirement is a single API key that fronts hundreds of LLMs with provider failover, normalized JSON, and credit-based billing. It is not a self-hosted gateway and it does not aim to compete on observability depth.

Architecture: OpenRouter exposes an OpenAI-compatible API across 200+ models from OpenAI, Anthropic, Google, Mistral, Meta, AWS Bedrock, Azure, Together, DeepSeek, Cohere, and many more. Routing, failover, normalization, and quota live on the OpenRouter side. The docs describe transformer settings, parameter mapping, response formats, and per-model rate limits. Client libraries on GitHub are open source even though the platform is not.

Pricing: OpenRouter is pay-as-you-go. You buy credits and provider calls deduct the underlying provider price (no markup on provider pricing). The platform charges a 5.5% credit purchase fee, with separate BYOK rules: the first 1M requests per month are free, then a 5% BYOK fee applies. There is no monthly platform fee for individual usage. Team accounts, bulk credits, and enterprise contracts are available.

Best for: Pick OpenRouter if your team wants one API key, one billing relationship, and access to many models without managing per-provider credentials. It is a fast path for prototypes, hackathon-grade apps, and teams that want to compare model behavior without buying a gateway product.

Skip if: Skip OpenRouter if you need self-hosted control, deep observability dashboards, prompt management, evals, simulation, or guardrails. It is a unified API plus credits, not a platform. Also model the 5.5% credit purchase fee plus the separate BYOK fee rules at your expected volume.

6. LangSmith: Best if your runtime is LangChain

Closed platform. Open-source SDKs and frameworks around it. Cloud, hybrid, and Enterprise self-hosting.

LangSmith is the strongest alternative when LangChain or LangGraph is the runtime. It is not a gateway-first product, but it covers tracing, evals, prompts, and Fleet workflows in a way that matches LangChain semantics. Pair it with LiteLLM, Portkey, or OpenRouter for routing.

Architecture: LangSmith covers Observability, Evaluation, Deployment through Agent Servers, Prompt Engineering, Fleet, Studio, and CLI. Enterprise hosting can be cloud, hybrid, or self-hosted, with self-hosted data in your VPC. The self-hosted v0.13 release on January 16, 2026 added the Insights Agent, revamped Experiments, IAM auth, mTLS for external Postgres, Redis, and ClickHouse, KEDA autoscaling, and IngestQueues enabled by default.

Pricing: Developer is $0 per seat per month with up to 5,000 base traces. Plus is $39 per seat per month with up to 10,000 base traces, one dev-sized deployment, unlimited Fleet agents, 500 Fleet runs, and up to 3 workspaces. Base traces cost $2.50 per 1,000 after included usage; extended traces cost $5.00 per 1,000 with 400-day retention.

Best for: Pick LangSmith if you use LangChain or LangGraph heavily and want native trace semantics, Prompt Hub, and Fleet workflows. Pair with LiteLLM or Portkey for the gateway part.

Skip if: Skip LangSmith if open-source platform control is non-negotiable, if seat pricing penalizes cross-functional access, or if your stack is mostly non-LangChain. The gateway side is not the focus.

Decision framework: Choose X if…

• Choose FutureAGI if your dominant workload requires gateway plus deep observability plus evals plus simulation plus guardrails in one open-source stack. Buying signal: you already run Helicone for routing, Langfuse or Braintrust for evals, and a separate guardrail tool, and they drift in production. Pairs with: OTel, OpenAI-compatible HTTP, BYOK judges, and self-hosted deployment.
• Choose Portkey if your dominant workload is a hosted enterprise gateway with budgets, virtual keys, and governance. Buying signal: you want a polished gateway and your eval workflow lives elsewhere. Pairs with: Langfuse for tracing, prompt experiments in a separate tool, and SOC 2 / HIPAA contracts.
• Choose LiteLLM if your dominant workload is a Python proxy at the SDK boundary. Buying signal: your codebase is already OpenAI client SDK calls and you want to swap providers without rewriting app code. Pairs with: Langfuse, Braintrust, or FutureAGI as the analytics and eval layer.
• Choose Langfuse if your dominant workload is OSS observability with prompts and datasets, and you do not need a gateway in the same product. Buying signal: you want trace data in your own infrastructure. Pairs with: LiteLLM proxy and a separate gateway tool.
• Choose OpenRouter if your dominant workload is one API key for many models without managing provider credentials. Buying signal: prototypes, hackathons, or fast model comparisons. Pairs with: any client SDK and a separate analytics tool.
• Choose LangSmith if your dominant workload is LangChain or LangGraph applications. Buying signal: your team already debugs chains and graphs in the LangChain mental model. Pairs with: LiteLLM or Portkey for the gateway part.

Common mistakes when picking a Helicone alternative

• Treating “gateway” and “observability” as the same product. Helicone is one of the few products where they are deeply combined; most alternatives pick one. If you keep Helicone for the gateway and add Langfuse or FutureAGI for traces, agree on which platform owns latency, cost, and token attribution.
• Picking a gateway by sticker price. The compound cost is provider token spend plus platform fee plus overage plus retention plus seats. OpenRouter’s 5.5% credit purchase fee plus BYOK rules and Portkey’s per-log fee both scale with traffic. LiteLLM’s free tier is real, but the ops cost of running a self-hosted proxy at scale is also real.
• Skipping the security review on a self-hosted gateway. Any service that holds provider API keys, sees PII, and routes traffic is in scope for SOC 2, HIPAA, or PCI in regulated industries. Verify each alternative’s compliance posture before traffic flows.
• Migrating without freezing the trace shape. Trace IDs, span IDs, attribute names, timing fields, and cost fields differ across platforms. If you migrate without locking the schema first, dashboards, alerts, and incident playbooks break quietly.
• Ignoring fallback policy. A gateway without a fallback policy is a single point of failure. Helicone, Portkey, LiteLLM, and FutureAGI all support retries and provider failover. Configure them before the first incident.
• Treating maintenance mode as immediate risk. Helicone is in maintenance mode, not deprecated. The right reading is to plan a 6 to 12 month evaluation window, not to migrate this quarter.

What changed in the LLM gateway landscape in 2026

How to actually evaluate this for production

1. Run a real traffic mirror. Mirror 5 to 10 percent of production requests through each candidate gateway for 7 days. Compare p50, p95, and p99 latency, success rate, retry rate, fallback rate, cost per 1,000 requests, and token attribution. Synthetic load tests do not reproduce the traffic shape that matters.

2. Inspect the trace contract. The gateway and the observability backend must agree on trace ID, span ID, attribute names, request and response payload shape, and cost fields. Mismatches break dashboards and alerting silently. If the gateway and the observability layer are different products, lock the contract before traffic flows.

3. Cost-adjust for your real mix. Real cost is platform fee plus provider token spend plus overage plus retention plus seats plus security review hours. A gateway with a low monthly fee can lose at production volume to a unified product that charges per gigabyte. A self-hosted proxy with $0 license can lose to a hosted gateway when on-call hours are added.

How FutureAGI implements gateway-first observability

FutureAGI is the production-grade gateway-plus-observability platform built around the closed reliability loop that Helicone alternatives stitch together by hand. The full stack runs on one Apache 2.0 self-hostable plane:

• Gateway, the Agent Command Center fronts 100+ providers with BYOK routing, fallback, latency-aware load balancing, and request caching; cost attribution rolls up per virtual key, per team, per model on the same plane.
• Tracing, traceAI (Apache 2.0) auto-instruments 35+ frameworks across Python, TypeScript, Java, and C#, and gateway spans land in the same trace tree as agent and retriever spans for end-to-end attribution.
• Evals and guardrails, 50+ first-party metrics attach as span attributes on every gateway request; 18+ runtime guardrails (PII, prompt injection, jailbreak, tool-call enforcement) enforce policy on the same plane, with turing_flash running guardrail screening at 50 to 70 ms p95.
• Simulation and prompt optimization, persona-driven scenarios exercise the gateway path before live traffic, and six prompt-optimization algorithms consume failing trajectories as training data so production regressions feed back into versioned prompts that the CI gate evaluates against the same threshold.

Pricing starts free with a 50 GB tracing tier, 100,000 gateway requests, and 100,000 cache hits; Boost is $250 per month, Scale is $750 per month with HIPAA, and Enterprise from $2,000 per month with SOC 2 Type II.

Most teams comparing Helicone alternatives end up running three or four tools in production: one for the gateway, one for traces, one for evals, one for guardrails. FutureAGI is the recommended pick because gateway, tracing, evals, simulation, and guardrails all live on one self-hostable runtime; the loop closes without stitching.

Sources

• Helicone pricing
• Helicone joining Mintlify
• Helicone GitHub repo
• FutureAGI pricing
• FutureAGI changelog
• Agent Command Center docs
• Portkey pricing
• Portkey Gateway repo
• LiteLLM repo
• LiteLLM Cloud pricing
• Langfuse pricing
• Langfuse self-hosting docs
• OpenRouter docs
• OpenRouter pricing
• LangSmith pricing

Series cross-link

Next: Langfuse Alternatives, Best LLM Gateways 2026, Portkey Alternatives, LangSmith Alternatives