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Helicone Alternative2026-09-16

Helicone vs LiteLLM in 2026: Which to Pick, and What Neither Does for Finance

Search for an LLM proxy and two names come up more than any other: Helicone and LiteLLM. They get compared constantly, which is odd, because they were built to do different jobs. Helicone is an observability product that happens to be a proxy. LiteLLM is a routing gateway that happens to track spend. Teams that pick one expecting the other's strengths tend to find out at month end.

Two things changed in 2026 that make the comparison worth redoing. In March, Helicone was acquired by Mintlify and moved into maintenance mode. And the question finance teams are asking of these tools has moved on from "what did we spend" to "what did each feature, team and customer cost, and does it reconcile to the invoice". Neither tool was designed around that second question, and it is worth being clear about why.

The short answer

If you need routing logic, fallbacks between providers, load balancing, and hard budget caps enforced on your own infrastructure, LiteLLM is the stronger tool and is actively developed.

If you need to see prompts and responses, debug what a model actually said, and score outputs, Helicone did that well, but it is no longer being built. Its owner has said it will help customers move to other platforms, so the sensible move for anything new is to treat it as a migration source rather than a destination.

If what you actually need is cost attribution that finance can use, by team, feature, environment and customer, reconciled to what the providers bill, then you are shopping in a third category, and the rest of this post explains where the first two fall short of it.

What Helicone is

Helicone started as a one-line proxy: change the base URL in your OpenAI client, and every request is logged with its prompt, response, latency, token counts and cost. On top of that log it built observability features: sessions, custom properties for tagging requests, prompt versioning, caching, and a dashboard for browsing what the model said. Later it added a self-hosted Rust gateway for teams that did not want the hosted proxy in their request path.

The important structural fact is that Helicone stores prompts and responses. That is the feature. Debugging a hallucination or comparing two prompt versions needs the content, so the content is kept. For a lot of engineering teams that is exactly right. For security reviews, and for any team handling customer data through the model, it is a corpus that has to be defended.

On 3 March 2026 Helicone joined Mintlify. The product remains live and, per both companies, will keep receiving security patches, bug fixes and new model support, but no new features. Mintlify has said it will work with customers on migrating to other platforms. That is an unusually direct signal from an acquirer.

What LiteLLM is

LiteLLM is an open-source gateway that presents one OpenAI-compatible endpoint in front of a very long list of providers, currently over 140 by its own count. The routing layer is the product: model fallbacks, load balancing across deployments, retries, rate limits, and semantic caching. You run it yourself, typically as a container with a Postgres database behind it, and Redis once you scale past a single instance.

Spend tracking is built in and is genuinely good for what it is. LiteLLM issues virtual keys, tracks cost per key, user, team and organisation, and supports hard budgets that stop requests once a cap is reached. Tag-based spend reporting, single sign-on, role-based access and audit logs sit behind the Enterprise licence. Prompt logging is configurable: you decide whether request content is stored and where it goes.

The structural fact here is operational. LiteLLM is infrastructure you own. That is a feature for teams who want control, and a cost for teams who did not want another service to patch, scale and back up.

Head to head

DimensionHeliconeLiteLLM
Primary jobLLM observability and prompt debuggingRouting gateway with spend controls
Status in 2026Maintenance mode since March 2026: security, bug fixes and new models onlyActively developed open source
IntegrationBase URL change (hosted proxy) or self-hosted gatewayBase URL change to your self-hosted proxy
ProvidersThe major providers140+ by LiteLLM's count
Routing, fallbacks, load balancingLimitedExtensive, the core of the product
Prompt and response storageYes, by designConfigurable
BudgetsBasicHard caps per key, team, organisation and model; requests stop at the cap
AttributionCustom properties on requestsTags and metadata; tag-based reports on Enterprise
Infrastructure you runNone on hosted; a Rust gateway if self-hostedGateway, Postgres, and Redis at scale
Reporting audienceEngineersEngineers and platform teams
LicenceOpen core, hosted plansOpen source, Enterprise licence for SSO, RBAC and tag reports

Where Helicone wins

Prompt-level debugging. If the question is "what did the model say to this user and why", Helicone's log is the answer, and a routing gateway will not give it to you. Sessions that stitch multi-step agent traces together are useful for the same reason. If you are already on Helicone and only need it for engineering debugging, maintenance mode is not an emergency. Security patches and model support are the two things that would break a proxy, and both are still shipping.

Where LiteLLM wins

Anything to do with resilience and control of the request path. If one provider degrades, LiteLLM fails over. If you run the same model on two clouds, it balances between them. If a team must not exceed a monthly cap, LiteLLM will refuse the request that would breach it. Breadth of provider support is unmatched. And it is being built, which in a category that ships new models weekly is not a small thing.

The trade is that all of this is yours to operate. Budget state lives in your database, which means the database is now on your critical path for every LLM call.

What neither does for finance

Here is the gap that the Helicone vs LiteLLM debate usually skips over.

Both tools report to engineers. Their dashboards and APIs answer engineering questions: which key spent what, which model was slow, which prompt version performed better. Finance asks different questions. What did the AI tier cost per customer last month. Which cost centre gets charged for the increase in the OpenAI bill. What is the gross margin on the feature we launched in Q2. Does the number on the dashboard agree with the number on the invoice, and if not, why.

Three things make those questions hard for either tool.

Token semantics are not uniform across providers. Anthropic reports input tokens exclusive of cache reads and cache writes, as three separate buckets. OpenAI and Gemini report cached tokens as a subset of prompt tokens. DeepSeek reports a cache hit and miss pair that adds up to the prompt count. A cost tracker that treats all of those as "prompt tokens" will double-count on one provider and under-count on another, and the error only shows up when someone reconciles to the invoice. Both Helicone and LiteLLM capture usage; neither was designed with reconciliation as the goal.

Attribution has to be captured at request time, and it has to be complete. Both tools let you tag requests. Neither requires it. An attribution model where 70 percent of requests carry a team tag is not a finance model, because the remaining 30 percent land in "unallocated" and finance has to apportion it by guesswork.

Prompt storage is a liability finance did not ask for. To answer the cost question you need token counts, model, timing and dimensions. You do not need the prompt. Helicone stores it by design; LiteLLM stores it if configured. A cost platform that never sees or keeps the content has nothing to defend in a security review, which for regulated teams is the difference between a two-week and a two-quarter procurement.

The third category

That gap is what AI SpendOps is built for. It is a managed gateway on Cloudflare's edge, so there is nothing to run. It captures prompt, completion, cached, cache-write and reasoning tokens as separate quantities and prices each against the provider's own rate card, with the provider-specific cache semantics handled so the totals reconcile. It requires the attribution dimensions you choose (team, feature, environment, customer) and can reject untagged requests at the gateway so the allocation is complete rather than sampled. It never stores prompt or response content. Budgets are tracked and forecast with burn alerts; they are not enforced by blocking traffic, which is a deliberate choice for a finance tool.

It is also worth saying what it is not. It does not do routing, fallbacks or load balancing; requests pass through unchanged. If you need those, LiteLLM is the better tool, and the two can sit together with LiteLLM routing behind the finance layer.

A decision guide

  • You need routing, failover and hard spend caps on infrastructure you control: LiteLLM.
  • You need to read prompts and responses for debugging, and you are already on Helicone: stay for now, plan a migration, and do not start new projects there.
  • You need prompt debugging and you are starting fresh: look at Langfuse or Braintrust rather than Helicone, given the roadmap.
  • You need finance-grade cost attribution, invoice reconciliation, and no prompt storage: AI SpendOps, or another gateway built around the cost question rather than the routing one.
  • You need two of the above: that is normal. Gateways chain. Pick the finance layer for the numbers and the routing layer for the traffic, and keep them separate.

Further Reading

Sources