What each one is
Helicone is an open-source LLM observability proxy. It sits between your service and upstream providers, logs every request, caches responses, applies rate limits, and gives you dashboards. Self-host or use the managed cloud. Particularly strong at high-throughput use cases.
SchneeAI is a platform for operating AI across multiple services. It includes a proxy (LiteLLM underneath), but the harder problems are per-tenant authentication, credit and budget enforcement, raw retention (Vault), PII scanning, audit, and dataset building.
Where they overlap
Both will:
- Sit between your service and upstream LLM providers
- Log structured request/response data
- Track cost and token usage
- Apply rate limits
- Cache responses (Helicone’s caching is more mature)
- Provide dashboards
- Be self-hostable
If your needs are logging + caching + rate limits, Helicone is excellent and easy to operate.
Where they differ
| Concern | Helicone | SchneeAI |
|---|---|---|
| Provider proxy / gateway | ✅ (custom proxy) | ✅ AI Gateway (LiteLLM) |
| Real-time policy blocking | Rate limits + basic rules | ✅ Pre-call PII/budget/credit checks |
| Caching | ✅ Mature (semantic cache option) | ✅ Response cache + scope controls |
| Credit ledger & billing | ❌ | ✅ Append-only, reservations |
| Hard budgets with auto-actions | ❌ (alerts only) | ✅ Block, throttle, kill switch |
| Prompt Registry + canary | ❌ | ✅ + canary rollout |
| Raw prompt/output Vault | Logging only | ✅ Encrypted, configurable retention |
| PII / secret scanning | ❌ (post-hoc) | ✅ 17 categories pre-call |
| Tenant isolation model | Multi-tenant | JWT/JWKS + tenant scope + RLS |
| Consent & dataset building | ❌ | ✅ Consent gates + lineage |
| License | Apache 2.0 (open source) | Mixed (open-sourcing in stages) |
The architecture question
Helicone is a proxy that observes and caches. SchneeAI is a platform that proxies and operates.
Your service → Helicone → OpenAI / Anthropic / Google / ...
↑
├── Logging
├── Caching
├── Rate limits
└── Dashboards
vs.
Your service → SchneeAI Gateway → LiteLLM → OpenAI / Anthropic / Google / ...
↑
├── Credit & per-tenant billing
├── Prompt Registry (canary, audit)
├── Budgets & kill switch
├── Vault (encrypted raw)
├── PII scanning (pre-call)
└── Console + BFF
If logging and caching are most of what you need, Helicone ships quickly. If you need platform-level governance, billing, and audit, SchneeAI is the answer.
When to pick Helicone
- You want a fast, open-source observability proxy.
- Caching (including semantic cache) materially cuts your bill.
- Rate limiting per key is enough — you don’t need credit ledgers.
- Budget enforcement and audit aren’t on your roadmap.
- You’re comfortable operating the proxy yourself or using their cloud.
Helicone is particularly good at high-throughput cases — request volumes where caching pays for the operational overhead.
When to pick SchneeAI
- Multiple services or tenants share AI infrastructure.
- Finance needs accurate per-tenant cost attribution and hard budgets.
- Operations needs a kill switch and per-tenant rate limits.
- Compliance requires retention windows, audit trails, and PII handling.
- You’re building training datasets from your own interactions.
Migration path
Helicone and SchneeAI coexist cleanly. The integration pattern:
- Keep Helicone as your proxy + cache layer.
- Stand up SchneeAI in front of Helicone — SchneeAI Gateway routes to Helicone as an upstream.
- SchneeAI handles credit reservation, PII scan, and audit before the call.
- Helicone handles caching and detailed logging.
- Move prompts into SchneeAI’s Registry if you need canary or audit.
You get caching from Helicone and governance from SchneeAI. The two systems don’t fight each other.
Cost comparison
- Helicone open-source: free; you operate it.
- Helicone Cloud: usage-based.
- SchneeAI: per-seat + usage, credit-based. Includes platform layer.
If you’re comparing line items, treat Helicone as proxy + cache and SchneeAI as platform. The two coexist for teams that need both.
FAQ
Does SchneeAI cache responses? Yes — there’s a response cache with scope controls (per-tenant, per-user). It’s simpler than Helicone’s semantic cache; teams that need semantic caching should run Helicone alongside.
Can I use Helicone as SchneeAI’s upstream? Yes. Configure Helicone as the provider URL in SchneeAI’s gateway. SchneeAI’s PII scan, credit reservation, and audit all run first.
Does Helicone do PII scanning? Helicone can flag content post-hoc via user-created rules. SchneeAI scans before the call upstream, with 17 built-in categories and configurable flag/mask/block policies.
Is Helicone faster than SchneeAI? Helicone’s caching makes effective latency lower on cache hits. SchneeAI’s gateway adds a few milliseconds for PII scan and credit reservation. For most workloads, the difference is negligible.
Comparing tools for your stack? Read the product overview, or talk to us about your current setup.