SAVI SDK vs. Langfuse vs. Helicone: Where the Free Tiers Actually Differ
September 13, 2026
If you’ve looked at LLM observability tools in the past six months, you’ve probably run into the same problem we did: many comparison posts are already out of date. The space has changed quickly, including two acquisitions in just three months.
Mintlify acquired Helicone in March 2026. The product still works, and self-hosting is still available, but it is now in maintenance mode, with no new features being released.
ClickHouse acquired Langfuse in January 2026. Langfuse has kept its MIT licence, with no new pricing restrictions. Tracing, dashboards, prompt management, datasets and evaluations remain open and actively developed.
That context matters more than most feature checklists, so it’s worth putting it first.
What Langfuse and Helicone do well
Neither product is a straw man, and we’re not going to pretend otherwise.
Langfuse offers genuine depth in prompt management and evaluation. It includes LLM-as-a-judge, annotation queues and prompt experiments, all MIT-licensed since mid-2025. It also has useful PII-handling features: you can define a mask() function that runs over trace data before it is sent. The mask_otel_spans feature, added in June 2026, also covers third-party OpenTelemetry instrumentation. Regex patterns are available out of the box, while llm-guard can be used for more advanced anonymisation.
If your main priority is evaluation tooling and prompt iteration, Langfuse is the more complete product.
Helicone takes a proxy-based approach rather than relying on an SDK wrapper. That means you can usually adopt it with almost no code changes: point your API calls at Helicone’s endpoint instead of instrumenting the application directly.
That architecture also allows Helicone to operate at the network layer, which gives it capabilities an SDK cannot easily provide. Its semantic caching can recognise similar requests, not just identical ones, and can meaningfully reduce costs for repetitive queries. That is a real strength, and it is more advanced than SAVI SDK’s cache today, which only supports exact fingerprint matches.
So this isn’t a claim that SAVI does everything better. The difference is narrower and more specific.
Where SAVI SDK differs
local_mode is zero-infrastructure, not just self-hostable. Both
Langfuse and Helicone's self-hosted paths mean deploying real
infrastructure - Langfuse needs Postgres, ClickHouse, Redis, and S3-
compatible storage; Helicone recently simplified its own stack down to
four containers. That's a reasonable trade for a team running production
observability at scale. But if you just want to try a tool, or run
something in a constrained/offline environment, pip install "savi-sdk [openai]" and local_mode=True gives you the real thing - tokens, PII
flags, LSH fingerprint - printed straight to your terminal, with nothing
to deploy, no account, and zero network calls. It's a different category
of "local," not a smaller version of self-hosting.
PII masking is on by default, not opt-in. Every wrapper masks PII (via Presidio's NER-based detection) unless you explicitly turn it off - compared to Langfuse's masking, which is a function you write and wire up yourself. Worth being precise here too: this only protects what SAVI's own telemetry receives. Your actual prompt still goes to OpenAI/Anthropic/etc. completely unmodified, same as it would without any wrapper at all - this isn't a redaction layer for outbound provider traffic.
A companion package for a problem neither of them addresses: savi-loop-guard - a separate, zero-dependency package that detects AI agents stuck in a loop (retrying the same failing tool call, bouncing between two plans). Neither Langfuse nor Helicone ships anything like this; it's a narrow, specific gap we built for because we kept hitting it ourselves.
Provider breadth beyond the usual two. OpenAI, Anthropic, Azure OpenAI, AWS Bedrock, Cohere, Mistral, and Google Vertex AI, each with the same drop-in wrapper shape. (Caveat, in fairness to Helicone: its proxy architecture is provider-agnostic by construction - pointing any HTTP traffic at it works regardless of provider - so "breadth" isn't a clean win against a fundamentally different architecture, just against Langfuse's SDK-based approach.)
Cost tracking in AUD alongside USD, and carbon estimates per call - a narrower, Australia-informed angle neither competitor's docs mention.
The honest summary
| SAVI SDK | Langfuse | Helicone | |
|---|---|---|---|
| Try it with zero infrastructure | Yes (local_mode) | No (Postgres+ClickHouse+Redis+S3 to self-host) | No (4 containers to self-host) |
| PII masking, on by default | Yes | Opt-in, you write the function | Not a core feature |
| Response caching | Exact-match only | - | Semantic + exact-match (more capable) |
| Prompt management / evaluation depth | No | Yes, deep | Limited |
| Setup friction for existing code | Wrapper swap | SDK instrumentation | Proxy (near-zero code change) |
| Actively developed post-acquisition | - | Yes (MIT intact) | Maintenance mode |
| Agent loop detection | Yes (companion package) | No | No |
If you need serious prompt evaluation and experimentation, Langfuse is the more complete product.
If you want the least disruptive way to add observability to existing code - especially with semantic caching - Helicone’s proxy model is worth considering, regardless of its acquisition status.
If you want to try an observability tool in five minutes with no account and no infrastructure, or you’re dealing with agents that get stuck in loops, that is the gap SAVI was built to address.