Compare·Open-model inference and training platform
Continuum
Multi-agent workbench
VS
Fireworks
Open-model inference and training platform

Continuum vs Fireworks

A token supplier and a workbench, not two answers to the same question. The interesting overlap is who controls coding-agent spend.

Fireworks AI is an inference and training platform for open models. It sells serverless per-token inference, on-demand dedicated GPU deployments, reserved capacity, managed fine-tuning, and reinforcement learning, with customers including Cursor, Vercel, Notion, Sourcegraph, and Quora, and published wins such as Notion cutting latency from roughly 2 seconds to 350 milliseconds. In July 2026 it moved closer to this comparison with Fireworks Nexus, a drop-in layer aimed squarely at engineering AI spend: enterprise cost controls with US-hosted endpoints and zero data retention across 20 data centers, FireConnect (an Apache 2.0 one-line installer that remaps Claude Code, Codex, and OpenCode onto open models through Anthropic-compatible and OpenAI-compatible APIs), and FireRouter, a difficulty-aware router still in research preview that Fireworks says delivers 3 to 5x cost reduction and cut about a third off per-merged-PR cost in tests with Notion and Doximity. Continuum sits above all of that. It is the free workbench where the agent actually runs: worktree per session, plan gate before writes, per-hunk diff, PR merge, live quota gauges, and cost by repo. Fireworks is a supplier you can consume; Continuum is the cockpit you consume it from.

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Updated 2026-08-03 · Mac stable · Win/Linux desktop beta

Choose Continuum when

Pick Continuum when the consumer of inference is a coding agent and you want the workbench, the review loop, and the cost view around it, with hosted inference as a flat monthly fee instead of a per-token meter.

Choose Fireworks when

Pick Fireworks when you need fast open-model serving at API scale for your own product, dedicated GPU capacity, managed fine-tuning or RL, or a routing layer that moves routine coding traffic onto cheaper open weights.

Snapshot adjacent job
Dimension Continuum Fireworks
Product layer Workbench developers open Inference and training platform
Primary buyer Engineering team shipping repositories Product team serving models at API scale
Model story Run vendor agents under your own logins Open-weight catalog, DeepSeek, Kimi, GLM, Qwen
Repo contract Worktree + branch · plan gate · diff · PR Not in scope for an inference API
Coding-agent angle The workbench itself Nexus: FireConnect plus FireRouter preview
Cost shape Free BYOK · flat-fee hosted inference Per token, per GPU hour, per training token
Instrumentation Quota gauges · cost by repo/provider/model/day Usage and latency dashboards per API
Mac stable · web · iPhone · Watch · Win/Linux desktop beta · free app
01

Buy tokens vs operate agents

In Continuum

Ship a repository with agents you can supervise

09:15

Start Claude Code and Codex in separate worktrees on the same repo, each with its own branch and transcript.

11:40

Approve the better plan from the phone and interrupt the weaker run without going back to the host.

14:30

Review the winning diff hunk by hunk, revert one change, open the pull request, and watch its checks.

17:10

Read the quota gauge and the day's cost by repo before launching the next batch of sessions.

In Fireworks

Serve open models fast inside your own product

09:15

Benchmark a candidate open model on serverless and compare quality and latency against the closed API you use today.

11:40

Fine-tune it on your own data with LoRA, starting at $0.50 per million training tokens for models up to 16B.

14:30

Move steady traffic onto an on-demand deployment once per-token spend justifies a dedicated GPU hour.

17:10

If the workload is engineering AI spend, evaluate Nexus: install FireConnect, then ask about FireRouter access while it is in research preview.

02

Monday path

Monday with a supplier decision and a workbench decision

Install, connect, first session - steps you can run the same day.

01

Name the consumer. If your product calls models at API scale, that is a Fireworks question. If engineers are running coding agents, that is a workbench question.

02

For the product path, benchmark Fireworks serverless on your real prompts. Published rates run from roughly $0.05 per million input tokens on Nemotron Lightning 3.5 30B up to $1.74 in and $3.48 out on DeepSeek V4 Pro, so model choice moves the bill more than the vendor does.

03

If throughput is steady, price on-demand GPUs instead. H100 and H200 are $7.00 an hour and B200 is $10.00 an hour through 31 August 2026, with Fireworks stating increases of $1 to $3 an hour from 1 September, so check the live page before you model a year.

04

For the coding path, install Continuum, start one worktree session per ticket, and let Plan mode hold the agent read-only until someone approves.

05

Add a Fireworks key to the OpenCode connector if you want open models in that same workbench. The provider id is fireworks-ai and the variable is FIREWORKS_API_KEY.

06

Try the flat-fee alternative side by side. Continuum hosted inference at Plus $25, Max 100 $100, Max 200 $200, or Ultra $500 a month carries weekly allowances of $25, $100, $200, and $1,000, which is a different risk shape than a meter.

07

If Nexus is on your list, evaluate FireConnect and FireRouter separately. FireConnect is Apache 2.0 and shipping; FireRouter is in research preview and access runs through a demo booking.

08

Score them on their own axes: tokens per second and cost per million for Fireworks, sessions supervised safely and cost per repository for Continuum.

How Continuum turns inference into an operating loop

How Continuum turns any inference supplier into an operating loop

  • Continuum launches the provider's real agent CLI under the login, subscription, or API key you already hold, rather than reselling a model behind its own account.
  • Each managed session receives an isolated git worktree and branch before any edit lands, which is the containment a token API cannot provide.
  • Plan mode stays read-only until an explicit approval, and that approval can arrive from Mac, iPhone, or web against the same live session.
  • The Diff pane reads the repository index directly and supports per-hunk stage, revert, and commit before a pull request leaves the worktree.
  • The PR pane creates the pull request, shows checks and review state, and merges through the GitHub CLI on the host without detaching the result from the conversation.
  • Live quota gauges expose the 5h and weekly subscription windows, and local agent history is priced by repo, provider, model, and day.
  • Third-party suppliers arrive through the OpenCode connector, so a Fireworks key sits beside Claude, Codex, Cursor, and Grok sessions in one interface.
03

Inference platform and workbench matrix

Cells use product nouns on both sides. Continuum is the multi-provider workbench; they win where their loop is the product.

Capability Continuum Fireworks
End-user coding-agent workbench Mac, web, iPhone, Watch, Windows and Linux beta Inference and training APIs, not a workbench
Run Claude Code, Codex, Cursor, and GrokRemapping a harness is not the same as hosting the workbench First-class provider sessions under your logins FireConnect remaps Claude Code, Codex, OpenCode to its models
Per-session git worktree and branch isolation Real worktree plus branch per managed session No repository orchestration layer
Plan gate, per-hunk diff, PR merge in session Read-only plan, stage or revert, checks, merge Builder composes its own product experience
Fast serving of open-weight modelsServing performance is Fireworks' category Reaches open models through the OpenCode connector Serverless, on-demand, and reserved capacity
Managed fine-tuning and reinforcement learning Not a training platform LoRA and full-parameter SFT, DPO, and RL
Dedicated GPU capacityRates quoted through 31 August 2026 No GPU rental H100 and H200 at $7.00/hr, B200 at $10.00/hr
Routes coding traffic to cheaper modelsBoth are routing claims; verify against your own traffic Auto model router on hosted inference FireRouter is in research preview
Flat monthly price for hosted inference $25, $100, $200, $500 with weekly allowances Usage based per token, per GPU hour, per training token
Cost attributed by repository Repo, provider, model, and day Nexus adds team and company budgets and ROI tracking
Live provider subscription quota gauges 5h and weekly windows read live Metered API, so there is no subscription window
Usable together Fireworks key via the fireworks-ai OpenCode provider OpenAI-compatible endpoint accepts a workbench client
04

Where supplier and cockpit separate

01 · Layer

Fireworks sells the tokens. Continuum is where the work happens.

An inference platform returns completions. A workbench decides which agent runs, in which worktree, on which branch, under whose approval, and whether the resulting diff gets merged. Those are not competing claims, and a team can hold both. The question is only which one you are missing today, and if the open question is instead which open checkpoint to serve, the DeepSeek model hub carries a card per checkpoint, including the V4 Pro and Flash variants Fireworks serves.

02 · The Nexus overlap

Nexus is the one place these two products argue.

Fireworks Nexus targets exactly the axis Continuum's org layer targets: engineering AI spend. It brings budgets at team and company level, ROI tracking across models and tools, and a router that pushes routine coding work onto open weights. The honest boundary is what it does not bring. FireConnect remaps a harness to cheaper models; it does not give you worktree isolation, a plan gate, per-hunk review, or PR merge. And FireRouter, the part carrying the 3 to 5x claim, is described as a research preview.

03 · Cost shape

A meter and a flat fee fail in different directions.

Fireworks is usage based: per million tokens on serverless, per GPU hour on dedicated, per million training tokens on fine-tuning, with $1 in free credits to start. That is efficient when load is predictable and unpleasant when an agent loops. Continuum's hosted inference is a flat monthly fee with a weekly allowance, so the ceiling is known in advance. Pick the failure mode you would rather explain to finance.

04 · Verify the numbers before you model a year

Fireworks reprices on a schedule, and it says so.

The GPU rates quoted here (H100 and H200 at $7.00 an hour, B200 at $10.00, B300 and GB300 between $12 and $18) are the rates published through 31 August 2026, with Fireworks stating an increase of $1 to $3 an hour from 1 September. Per-token rates also move as models are added. Read the live pricing page before committing to an annual model; that applies to every vendor on this site, and we say it here because Fireworks publishes the change date openly.

05 · Complement

The pairing is real, not a courtesy sentence.

Continuum's OpenCode connector carries Fireworks as provider id fireworks-ai against https://api.fireworks.ai/inference/v1/ with a FIREWORKS_API_KEY, exposing 23 models today. So a Fireworks key genuinely runs inside the workbench beside your Claude and Codex sessions, and the same per-repo ledger prices it. This is a supported path, not a hypothetical integration.

05

Coexistence

Stack recipe

How people run both.

Use both. Continuum reaches Fireworks through its OpenCode connector: the models.dev provider id is fireworks-ai, the endpoint is https://api.fireworks.ai/inference/v1/, and the credential is a FIREWORKS_API_KEY, which carries 23 models today. Add the key once and Fireworks models appear alongside your Claude, Codex, Cursor, and Grok sessions in the same workbench, priced per token by Fireworks and attributed per repo by Continuum. If you would rather not meter tokens at all, Continuum's own hosted inference is the flat-fee path.

Download Continuum Providers
06

Pick by who consumes the tokens

Continuum

Choose Continuum when a coding agent is the consumer

  • The consumer of inference is a coding agent, not your product's runtime.
  • You need worktree isolation, a plan gate, diff review, and PR merge around the model.
  • You want a predictable flat monthly fee with a weekly allowance instead of a token meter.
  • You want the same live session on Mac, iPhone, and web rather than an API client.
  • You want cost attributed to a repository and a provider window.
  • You would rather bring a Fireworks key into one workbench than build a second interface.
Fireworks

Choose Fireworks when your product is the consumer

  • Your product serves open models at API scale and latency is the binding constraint.
  • You need managed fine-tuning, DPO, or reinforcement learning on your own data.
  • Steady throughput justifies dedicated or reserved GPU capacity.
  • You want to move routine coding traffic onto open weights and are willing to pilot a preview router.
  • US-hosted endpoints with zero data retention are a requirement for the workload.
  • You measure success in tokens per second and cost per million rather than sessions supervised.
07

Per token versus flat fee

Continuum

App + your labs

$0 for the workbench on Mac, web, iPhone, and Watch, running under the subscriptions and provider keys you already hold, with no cut taken on those sessions. Optional hosted inference is Plus at $25, Max 100 at $100, Max 200 at $200, and Ultra at $500 per month, carrying weekly allowances of $25, $100, $200, and $1,000, reachable at an OpenAI-compatible endpoint and an Anthropic-compatible bare origin with cont_sk_ keys.

Fireworks

Their bill

Fireworks is usage based and starts with $1 in free credits. Serverless per-token rates published today include DeepSeek V4 Flash at $0.14 in and $0.28 out per million, GLM 5.2 at $1.4 in and $4.4 out, and DeepSeek V4 Pro at $1.74 in and $3.48 out. On-demand GPUs run $7.00 an hour for H100 and H200 and $10.00 for B200 through 31 August 2026, with Fireworks stating a $1 to $3 hourly increase from 1 September. Managed fine-tuning starts at $0.50 per million training tokens for LoRA SFT on models up to 16B. Nexus access is arranged through a demo request rather than a published tier.

How to compare

Total cost of work

These are different meters, not different prices for the same thing. Continuum charges nothing for BYOK sessions and sells a flat monthly inference allowance; Fireworks sells tokens, GPU hours, and training capacity. A team that wants both simply pays each for what it does.

Fireworks plans checked 3 August 2026 against their pricing page. Vendors reprice often - if you spot a stale figure, tell us and we will fix it. Continuum never marks up a session it runs under your own login.

Continuum ladder: Free app · Plus $25/mo · Max 100 · Max 200 · Ultra - full pricing. Also analytics, multi-account, devices.

08

Questions

Continuum vs Fireworks.

Deep dives: docs, providers, sessions.

Fireworks AI is an inference and training platform for open models. It offers serverless per-token inference, on-demand dedicated GPU deployments, reserved capacity, managed fine-tuning including LoRA and full-parameter training, and reinforcement learning. Named customers include Cursor, Vercel, Notion, Sourcegraph, and Quora.

Nexus is Fireworks' layer for engineering AI spend, introduced in July 2026. It has three parts: enterprise cost controls with US-hosted endpoints, zero data retention, and 20 global data centers; FireConnect, an Apache 2.0 one-line installer that remaps Claude Code, Codex, and OpenCode onto Fireworks models via Anthropic-compatible and OpenAI-compatible APIs; and FireRouter, a difficulty-aware router in research preview that Fireworks says delivers 3 to 5x cost reduction and cut roughly a third off per-merged-PR cost in tests with Notion and Doximity.

Yes. Continuum reaches Fireworks through its OpenCode connector using the models.dev provider id fireworks-ai, the endpoint https://api.fireworks.ai/inference/v1/, and a FIREWORKS_API_KEY, which carries 23 models today. Add the key once and those models sit beside your Claude, Codex, Cursor, and Grok sessions in the same workbench.

Only if what you actually wanted was a workbench. Continuum does not serve open models at API scale, rent GPUs, or fine-tune. It runs and supervises coding agents. The overlap is narrower and more specific: both have an answer for controlling coding-agent spend, and Continuum's answer is a flat-fee hosted plan plus per-repo attribution rather than a router in front of a token meter.

It is usage based with $1 in free credits. Serverless is per million tokens and varies widely by model, from about $0.05 in on Nemotron Lightning 3.5 30B to $1.74 in and $3.48 out on DeepSeek V4 Pro. On-demand GPUs are $7.00 an hour for H100 and H200 and $10.00 for B200 through 31 August 2026, rising $1 to $3 an hour from 1 September per Fireworks' own note. Fine-tuning starts at $0.50 per million training tokens.

Measure it rather than assuming. A Claude or ChatGPT subscription is prepaid, so a marginal turn on it costs nothing until you hit the window; open-model routing converts that into a metered bill that may or may not be lower. Continuum's quota gauges and per-repo ledger give you both halves of that comparison, and the same workbench runs either configuration.

Begin

Run your agents
in Continuum.

Free app. Your subscriptions. Optional hosted inference. Mac stable - Windows and Linux desktop are beta.

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