Same budget.2.5× more coding sessions.
The intelligent gateway between any coding agent and any model. It strips repeated context in memory — up to 85.7% fewer tokens — so the budget you already pay lasts many more sessions, with zero agent changes.
export ANTHROPIC_BASE_URL="https://api.tryvelora.it"Dispatches multi-turn conversation with full duplicate context.
Receives pure active code and essential types only.
Frontier resultsfor a fraction of the cost.
A frontier model makes high-stakes architectural decisions while a lean sidekick handles repetitive file edits and test loops. With velora-auto, you pay frontier token prices only where reasoning depth matters.
Lower context cost than Claude Code direct with Claude Opus 5.
Lower billed cost than Codex direct with Claude Sonnet 5.
How Velora protects your budget.
Four interconnected mechanisms working silently in-memory between your agent and the model pool.
Your agent sends everything. Velora removes the noise.
Coding agents (Cursor, Claude Code, OpenCode) re-transmit your entire file history, stale terminal dumps, and old tool results on every interaction. You end up paying for the same context repeatedly while the model lags. Velora synthesizes the request in-memory, stripping up to 85.7% of the tokens before they are billed.
- Prunes stale terminal buffers and repetitive file reads
- Preserves function signatures, types, and code intent
- Evaluated across 246 real agent turns with pass-rate parity (Delta 0.0pp)
Anchor the prefix. Never pay twice for setup.
Every time an agent appends a chat line, standard providers risk evicting prompt caches if headers or tool structures shift. Velora anchors the foundational repository context, keeping the session prefix stable for provider-native caching across your multi-turn session.
- Stable prefix for provider-native caching
- No re-processing of the anchored setup context
- Cuts billed costs on long multi-file refactors
Repo map, system prompt & tools definition frozen in upstream KV cache.
The right model for every turn. Automatically.
Simple syntax edits and quick test passes don't require expensive frontier reasoning models; complex database migrations and architectural refactoring do. Our semantic router classifies each turn's complexity on the fly and dispatches it to the optimal model tier from the pool.
- Routine code modifications route to ultra-fast Instruct models
- Complex architectural planning promotes seamlessly to Frontier Reasoning
- Strict sequence integrity: zero broken tool loops or missing IDs
Change one base URL. Keep your existing workflow.
No custom IDE extensions, no local background daemons, and zero vendor lock-in. Point your existing agent to https://api.velora.ai/v1 and provide your API key. Works immediately with any OpenAI or Anthropic SDK-compatible tool.
- Drop-in setup in about 40 seconds
- Works with Cursor, Claude Code, OpenCode, Cline, Windsurf, and custom scripts
- Clean code invariant: zero watermarks or comments injected into your repos
https://api.velora.ai/v1Add Velora like a provider.Keep the goal in view.
In OpenCode, add Velora as if you were adding a new provider. The same base-URL setup works with Pi Agent, Claude Code, Cursor/Cline, and any OpenAI-compatible harness.
Your harness keeps the session goal alive as the conversation changes. Velora turns that moving goal into updated guidance for the model — reducing drift, bias, and hallucinations without changing how you work.
Provider-native setup
Type /con in OpenCode and select connect; configure the other harnesses with the same endpoint.
The goal moves with the session
When your intent changes, the guidance changes with it — keeping the model anchored to the current objective and the available evidence.
The command menu appears while you type. Velora is selected like any other integration; the session goal is refreshed from the conversation on every turn.
What changes for you.
Two distinct levers: lossless context compression on any model, plus autonomous multi-model routing.
Velora removes duplicated files and dead logs in memory, so you pay for a fraction of the context — with zero changes to your workflow.
Smaller prompts mean answers start in ~1 second instead of 5 — keeping long autonomous sessions real-time.
Nothing your model needs is ever removed: active code, types and intent survive byte-for-byte.
Same sessions. Smaller bill.
~20 agent sessions at ~$5 of context each.
−62.75% mean over a 246-turn lifecycle.
−89.1% billed, measured across live sessions.
Sources: −62.75% = token-weighted mean over a 246-turn agentic replay (10 sessions, DeepSeek-V4-Flash); −89.1% = billed cost over 90 live calls across the free/standard/premium tiers (BM6, $5.00 → $0.55), reported as a lower bound because provider prompt-caching is not exposed (BM2 = UNKNOWN) and not counted. Illustrative, not a price guarantee.
Calculate your team's annual ROI.
Choose between single-model compression or full autonomous routing.
Semantic Router Active: Dispatches routine edits to instruct models from the pool, reserving frontier reasoning for hard refactors. Measured −89.1% billed (BM6) — dropping your annual invoice from $1201 down to $132/yr.
≈ $132/ year
Every figure on this page comes from live multi-turn sessions measured under the BM-STAT methodology — exact token accounting, confidence intervals, paired quality tests. The full technical proofs are available in our benchmarks report.
For the rigorous.
Empirical measurement methods, the multi-layer pipeline, and full reproducible benchmarks.
On Turn 1, compression is 0%.
We don't magically compress novel user prompts. Velora's measured 60–85% token reduction begins on Turn 5+, as autonomous agents repetitively dump unchanged files, compiler stack traces, and already-consumed bash tool outputs.
Velora is a standard OpenAI & Anthropic wire proxy. If our gateway is ever degraded or you want to exit, revert your base URL in 5 seconds. No proprietary SDKs, no state held, no workflow disruption.
The benchmark, with method
Empirically validated on real agent turns with exact token accounting, latency profiling, and deterministic pass-rate parity. Raw path versus Velora path, same tasks.
| Metric | Without Velora | With Velora | Change |
|---|---|---|---|
| Agentic Trajectory Replay (246 turns) | 3,188,351 tok | 1,187,672 tok | −62.75% Mean (−75.33% Peak) |
| Full Pipeline Stack Reduction (BM1) | 3,230,000 tok | 497,000 tok | −84.61% (full pipeline) |
| Multi-Turn Codebase Review (velora-saas) | 77,677 tok | 30,217 tok | −61.1% Mean (−85.7% Peak) |
| Live Multi-Model Cost Savings (BM6) | $5.00 | $0.55 | −89.1% billed spend (lower bound) |
| Needle survival rate (NVIDIA RULER 128k) | 1.23M tokens | 10/10 preserved | 100.0% (0.00% expansion) |
| Deterministic Pass-Rate Parity (BM1) | 42.9% pass | 42.9% pass | Delta 0.0pp (14/14 identical) |
| Reusable-Answer Cache Hit Rate (BM9) | 0.0% | 84.2% hits | p95: 1.47 ms response |
| Gateway latency overhead (Isolated p50) | direct: 0 ms | 6.31 – 17.47 ms | p50: 17.47 ms (CPU: 5.8 ms) |
| Tool-call schema & JSON protocol integrity | standard proxies: ~2% | 0 errors / 246 calls | 100% strict pairing |
What happens inside, stage by stage
- 1
Ingest
Accepts standard OpenAI and Anthropic requests from any agent, unchanged.
- 2
Compress
Condenses repeated file history and terminal output. Slices up to 85.7% of tokens.
- 3
Cache anchor
Stabilizes the session prefix so provider-native prompt caches stay warm.
- 4
Auto-Route
With velora-auto, semantic routing dispatches routine turns to instruct models from the pool and hard turns to frontier models.
- 5
Loop guard
Keeps tool execution sequences unbroken across long autonomous runs.
- 6
Stream
Returns model output in real time, with exact post-compression usage attached.
How this compares to the alternatives
- Context optimization
- Verified output parity, up to 85.7% fewer tokens
- Statistical dropping (up to 30% fact loss) or arbitrary client-side message truncation.
- Model cost management
- velora-auto semantic routing across the model pool (measured ~89.1% session saving, BM6)
- Locked to full list price of expensive frontier models on every trivial turn.
- Information reversibility
- Verified output parity (identical agent results)
- Irreversible token dropping; broken indentation and mangled type definitions.
- Prompt caching
- Stable session lock for provider-native caching
- Manual cache configuration on raw APIs; not offered by proxies or editors.
- Client compatibility
- Drop-in base URL (Cursor, Claude Code, OpenCode)
- Vendor SDKs only, custom Python runtimes, or locked to a single editor.
- Data Governance
- RAM-only gateway + End-to-End ZDR design via velora-zdr (Regolo AI EU)
- 30-day provider retention or third-party proxy disk logging.
Supported models
- Claude Fable 5.1
claude-fable-5.1 - Claude Opus 5
claude-opus-5 - GPT-5.6 Luna
gpt-5.6-luna - GPT-5.6 Sol
gpt-5.6-sol - DeepSeek V4 Pro
deepseek-v4-pro-0813 - DeepSeek V4 Flash
deepseek-v4-flash-0731 - GLM 5.3 Flash
glm-5.3-flash - GLM 5.3
glm-5.3 - Qwen 3.8 2.4T A95B
qwen3.8-2.4t-a95b - Qwen 3.8 Flash
qwen3.8-flash - Gemini 3.7 Flash
gemini-3.7-flash - Gemini 3.6 Flash
gemini-3.6-flash - Grok 4.6
grok-4.6 - Grok 4.5
grok-4.5 - Nemotron Lightning 3.5 30B
nemotron-lightning-3.5-30b-a3b
Same price. More effective work.
Stop paying frontier rates for duplicate file dumps and compiler logs your agent already read. Start with our 14-day trial (200k tokens/day, ~5M provider equivalent) and scale with tiered efficiency that cuts unit costs up to 63%.
Full gateway access on day one. On day 14 you get a verified savings statement — then pick Solo, Pro or Ultra, or walk away with your data.
Switch to yearly and save up to €360/year (2 months free on every plan).
Solo
2.5× Work MultiplierFor individual developers
Market baseline for everyday agent coding, matching Cursor Pro pricing with multi-model routing.
- 8M measured tokens / month
- ~20M effective work
- Automatic redundancy removal + session memory
- Economy models for routine tasks
- Encrypted BYOK vault
Pro
Most popular · −48% unit costFor intensive coding sessions
Cuts effective token cost in half. Pays for itself on session #2 by eliminating duplicate tool logs. Unlocks coder models and semantic cache.
- 25M measured tokens / month
- ~100M effective work
- Reusable-answer cache + repeated-work detection
- Coder models for complex refactors
- Smart router picks the right model per task
Ultra
6.0× Max EfficiencyFor parallel agent swarms
Maximum efficiency for parallel multi-agent workflows and heavy reasoning models.
- 70M measured tokens / month
- ~420M effective work
- Multi-agent parallel + long-session memory
- Frontier models for architecture and reasoning
- 60-day audit history
Rate limits: Solo 120 RPM · Pro 300 RPM · Ultra 600 RPM · Team 1,000 RPM pooled. Annual billing saves 2 months on every plan.
Team
5.0× pooled · Min 3 seatsCollaborative governance, project keys, and shared pooled quota across developers. 20M tokens/seat pooled (~100M/seat effective work, €0.39/M), project keys, budget caps and pooled 1,000 RPM.
Enterprise
Custom Scale & VPCFor organizations requiring custom scale, dedicated VPC peering, custom rule training, and enterprise SLA.
- Unlimited seats & custom throughput quotas
- Dedicated VPC peering or Self-Hosted proxy
- Enterprise SSO/SAML, RBAC & custom export
- EU data residency, ZDR attestation & DPA
- Dedicated engineer · 99.95% uptime SLA
Dedicated solutions engineer onboard in <24h
Zero Bill Shock
No unannounced open-ended metered bills. Top up with controlled 1-click refill packs (Solo +4M €10, Pro +10M €20, Ultra +20M €30) or enable auto-refill with strict monthly caps.
Tiered Efficiency Value
As your agent workloads grow, your effective token costs drop. Pro cuts effective costs by 48% and Ultra cuts them by 63% compared to baseline.
Encrypted BYOK Vault
Bring your own OpenAI, Anthropic, or DeepSeek keys. Encrypted at rest with AES-256-GCM, auto-freeze on key revocation, and zero platform fee within quota.
Need custom volume, VPC peering or dedicated on-premise SLA? Contact our enterprise team.
Questions, answered.
Everything you need to know about the gateway mechanics, zero data retention, and compatibility.
velora-auto pick per turn, sending small edits to fast models and architecture work to frontier ones.Try it on your own code.
Join the private beta. Fourteen days on your real sessions, your real models. If the numbers don't convince you, walk away in 5 seconds — the savings statement is yours to keep.
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