Make your agents
faster — honestly.
Benchmark latency, find bottlenecks, and optimize real agent performance across prompts, tools, and model backends. No magic 80% speedup claims — just measurable wins.
From measurement to acceleration in four steps.
Connect
Add an OpenAI-compatible, Anthropic, OpenRouter, or self-hosted vLLM/SGLang endpoint.
Profile
Define agent profiles — prompts, tools, memory mode, token budget.
Benchmark
Run prompt sets and capture TTFB, total latency, tokens, cost, errors.
Optimize
Get rule-based recommendations with expected impact and apply guidance.
Where agent latency actually comes from.
Aggregate breakdown across hundreds of agent traces. Most teams over-index on model speed and under-index on workflow shape.
Real workflow acceleration — not magic.
Prompt slimming
Cut tokens that don't change outputs.
Model routing
Send simple tasks to faster/cheaper models.
Parallel tool calls
Stop running tools sequentially when you don't have to.
Semantic cache
Sub-100ms responses on repeated intent.
Token budget control
Cap max_tokens with structured output.
Speculative decoding *
Self-hosted only. See Spec Lab.
* Inference-level acceleration (speculative decoding) is only available for self-hosted / open-weight model stacks. We don't claim to accelerate closed hosted APIs at the inference layer.
Benchmark your stack against reality.
Upload a prompt set or write one inline. We capture TTFB, total time, tokens, cost, errors and surface the deltas across runs.
Spec Lab — speculative decoding for self-hosted models.
Configure draft/target model pairs against your self-hosted vLLM or SGLang endpoint, define a dataset, and orchestrate jobs. Designed to plug into DeepSpec / DSpark-style workflows on supported open-weight stacks.
Requires self-hosted / open-weight model infrastructure. Not applicable to closed hosted APIs.
Share what changed — and what it bought you.
Honest pricing for measurable wins.
- 1 workspace
- Up to 3 connectors
- Unlimited local benchmarks
- Rule-based recommendations
- Shared workspaces
- Run history & exports
- Priority recommendations
- Slack notifications
- On-prem deployment
- Spec Lab worker integration
- SSO & audit logs
- Solutions support
Honest answers.
Can SeekSpeed make OpenAI/Anthropic models faster?+
Not at the inference layer — closed hosted APIs run on the provider's hardware. What we do speed up: workflow shape, prompt size, tool concurrency, caching, routing, retries, and token budget. These usually yield the biggest real-world wins.
What about speculative decoding / DSpark?+
Available only for self-hosted / open-weight stacks (vLLM, SGLang). Spec Lab provides orchestration and a worker adapter. We don't pretend it applies to closed APIs.
Where are my API keys stored?+
By default v1 stores credentials in your browser's local workspace. For team/self-hosted, credentials live encrypted server-side.
Do I need to instrument my agent code?+
No — point a connector at the model endpoint and run prompts. You can also wire up traces from your agent framework via the API.
Stop guessing. Start measuring.
Spin up a workspace in seconds. Local-first, no card required.